From 3a8390ef04bc1c92aec6722ad1cc5c72f94d6899 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:07:54 +0000
Subject: [PATCH 01/17] constrained atoms fix

---
 apax/md/simulate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apax/md/simulate.py b/apax/md/simulate.py
index a29108a2..2cb7a64c 100644
--- a/apax/md/simulate.py
+++ b/apax/md/simulate.py
@@ -255,7 +255,7 @@ def run_sim(
         dynamics_checks,
     )
 
-    constraints = [FixAtoms(indices=[6, 8])]
+    # constraints = [FixAtoms(indices=[6, 8])]
 
     apply_constraints = create_constraint_function(
         constraints,

From 29a38268732f99973988aec6bd38c7b5d896d646 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:09:00 +0000
Subject: [PATCH 02/17] callback fix

---
 apax/train/callbacks.py | 3 +++
 apax/train/eval.py      | 1 -
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/apax/train/callbacks.py b/apax/train/callbacks.py
index 90825977..3acaea5e 100644
--- a/apax/train/callbacks.py
+++ b/apax/train/callbacks.py
@@ -45,6 +45,9 @@ def on_train_end(self, logs=None):
         for cb in self.callbacks:
             cb.on_train_end(logs)
 
+    def on_test_batch_end(self, batch, logs=None):
+        for cb in self.callbacks:
+            cb.on_test_batch_end(batch, logs)
 
 def format_str(k):
     return f"{k:.5f}"
diff --git a/apax/train/eval.py b/apax/train/eval.py
index e111a618..984551af 100644
--- a/apax/train/eval.py
+++ b/apax/train/eval.py
@@ -120,7 +120,6 @@ def predict(model, params, Metrics, loss_fn, test_ds, callbacks, is_ensemble=Fal
         0, test_ds.n_data, desc="Structure", ncols=100, disable=False, leave=True
     )
     for batch_idx in range(test_ds.n_data):
-        callbacks.on_test_batch_begin(batch_idx)
         batch = next(batch_test_ds)
         batch_start_time = time.time()
 

From 2bcbdf8a93b16aa6dea45da2bd2d3d1092edc450 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:09:36 +0000
Subject: [PATCH 03/17] current temp in nve kwargs fix

---
 apax/utils/jax_md_reduced/simulate.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/apax/utils/jax_md_reduced/simulate.py b/apax/utils/jax_md_reduced/simulate.py
index 4f238887..f590cf81 100644
--- a/apax/utils/jax_md_reduced/simulate.py
+++ b/apax/utils/jax_md_reduced/simulate.py
@@ -290,6 +290,7 @@ def init_fn(key, R, mass=f32(1.0), **kwargs):
     @jit
     def step_fn(state, **kwargs):
         _dt = kwargs.pop("dt", dt)
+        _ = kwargs.pop("kT")
         return velocity_verlet(force_fn, shift_fn, _dt, state, **kwargs)
 
     return init_fn, step_fn

From 59d8034a575f0b9b4e7d4c67899b2de9e9dcdae1 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:10:23 +0000
Subject: [PATCH 04/17] tutorial overhaul

---
 examples/01_Model_Training.ipynb       | 114 +++++----
 examples/02_Molecular_Dynamics.ipynb   | 124 +++++++---
 examples/03_Transfer_Learning.ipynb    | 144 ++++++------
 examples/04_Batch_Data_Selection.ipynb | 313 +++++++++++++++----------
 4 files changed, 421 insertions(+), 274 deletions(-)

diff --git a/examples/01_Model_Training.ipynb b/examples/01_Model_Training.ipynb
index 08d983bf..bdcaa16d 100644
--- a/examples/01_Model_Training.ipynb
+++ b/examples/01_Model_Training.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -107,11 +107,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!apax template train"
+    "!apax template train --full"
    ]
   },
   {
@@ -126,23 +126,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 validation error for Config\n",
+      "1 validation errors for config\n",
       "n_epochs\n",
-      "  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='<NUMBER OF EPOCHS>', input_type=str]\n",
-      "    For further information visit https://errors.pydantic.dev/2.6/v/int_parsing\n",
+      "  Input should be a valid integer, unable to parse string as an integer\n",
+      "  input_type: str\n",
+      "  input: <NUMBER OF EPOCHS>\n",
+      "\n",
       "\u001b[31mConfiguration Invalid!\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "!apax validate train config.yaml"
+    "!apax validate train config_full.yaml"
    ]
   },
   {
@@ -185,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,14 +195,14 @@
     "\n",
     "from apax.utils.helpers import mod_config\n",
     "\n",
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 100,\n",
     "    \"data\": {\n",
     "        \"n_train\": 990,\n",
     "        \"n_valid\": 10,\n",
-    "        \"valid_batch_size\": 1,\n",
+    "        \"valid_batch_size\": 10,\n",
     "        \"experiment\": \"ethanol_ccsd_t_cli\",\n",
     "        \"directory\": \"project/models\",\n",
     "        \"data_path\": str(train_file_path),\n",
@@ -208,17 +210,17 @@
     "        \"energy_unit\": \"kcal/mol\",\n",
     "        \"pos_unit\": \"Ang\",\n",
     "    },\n",
-    "    \"model\": {\"descriptor_dtype\": \"fp64\"},\n",
     "}\n",
+    "\n",
     "config_dict = mod_config(config_path, config_updates)\n",
     "\n",
-    "with open(\"config.yaml\", \"w\") as conf:\n",
+    "with open(\"config_full.yaml\", \"w\") as conf:\n",
     "    yaml.dump(config_dict, conf, default_flow_style=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -226,12 +228,12 @@
      "output_type": "stream",
      "text": [
       "\u001b[32mSuccess!\u001b[0m\n",
-      "config.yaml is a valid training config.\n"
+      "config_full.yaml is a valid training config.\n"
      ]
     }
    ],
    "source": [
-    "!apax validate train config.yaml"
+    "!apax validate train config_full.yaml"
    ]
   },
   {
@@ -245,32 +247,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 12:22:53 | Running on [cuda(id=0)]\n",
-      "INFO | 12:22:53 | Initializing Callbacks\n",
-      "INFO | 12:22:53 | Initializing Loss Function\n",
-      "INFO | 12:22:53 | Initializing Metrics\n",
-      "INFO | 12:22:53 | Running Input Pipeline\n",
-      "INFO | 12:22:53 | Read data file project/ethanol_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:22:53 | Loading data from project/ethanol_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:22:54 | Computing per element energy regression.\n",
-      "INFO | 12:22:54 | Initializing Model\n",
-      "INFO | 12:22:54 | initializing 1 models\n",
-      "INFO | 12:23:03 | Initializing Optimizer\n",
-      "INFO | 12:23:04 | Beginning Training\n",
-      "Epochs: 100%|█████████████████████████████████████| 100/100 [00:48<00:00,  2.07it/s, val_loss=0.105]\n",
-      "INFO | 12:23:52 | Finished training\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732123698.225144  453396 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732123698.228412  453396 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 17:28:20 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 17:28:20 | Initializing Callbacks\n",
+      "INFO | 17:28:20 | Initializing Loss Function\n",
+      "INFO | 17:28:20 | Initializing Metrics\n",
+      "INFO | 17:28:20 | Running Input Pipeline\n",
+      "INFO | 17:28:20 | Reading data file project/ethanol_ccsd_t-train_mod.xyz\n",
+      "INFO | 17:28:20 | Found n_train: 990, n_val: 10\n",
+      "INFO | 17:28:21 | Computing per element energy regression.\n",
+      "INFO | 17:28:21 | Building Standard model\n",
+      "INFO | 17:28:21 | initializing 1 model(s)\n",
+      "INFO | 17:28:28 | Initializing Optimizer\n",
+      "INFO | 17:28:28 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:28:36 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:47<00:00,  2.11it/s, val_loss=0.0237]\n",
+      "INFO | 17:29:15 | Finished training\n"
      ]
     }
    ],
    "source": [
-    "!apax train config.yaml"
+    "!apax train config_full.yaml"
    ]
   },
   {
@@ -296,14 +302,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epochs: 100%|█████████████████████████████████████| 100/100 [00:47<00:00,  2.12it/s, val_loss=0.105]\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732123756.998387  453263 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732123757.001735  453263 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:29:34 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:51<00:00,  1.93it/s, val_loss=0.0234]\n"
      ]
     }
    ],
@@ -311,7 +321,7 @@
     "from apax.train.run import run\n",
     "from apax.utils.helpers import mod_config\n",
     "\n",
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"data\": {\n",
@@ -326,12 +336,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -366,6 +376,7 @@
     "    axes[id].set_ylabel(f\"{key}\")\n",
     "    axes[id].set_xlabel(r\"epoch\")\n",
     "\n",
+    "plt.legend()\n",
     "plt.show()"
    ]
   },
@@ -381,14 +392,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Structure: 100%|███████████████████████████████| 999/999 [00:03<00:00, 280.74it/s, test_loss=0.0838]\n"
+      "Structure: 100%|████████████████████████████████| 999/999 [00:04<00:00, 234.76it/s, test_loss=0.018]\n"
      ]
     }
    ],
@@ -400,29 +411,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 214.87it/s, test_loss=0.0837]\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732123824.572919  455329 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732123824.576016  455329 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Structure: 100%|███████████████████████████████| 999/999 [00:03<00:00, 253.48it/s, test_loss=0.0218]\n"
      ]
     }
    ],
    "source": [
-    "!apax eval config.yaml"
+    "!apax eval config_full.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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SidjTp095+/iiKVOmMENDQy5OnfenMVKcZxU/nHr27MksLCzY/fv3ubI//viD6enpseDgYK5M1fmUnZ3N9PT02DvvvMP7YGSMcceguLiYmZmZsUmTJvHm5+fnM1NTU67833//ZQDYihUrXmk/FXWdPHkyV1ZeXs7atGnDBAIB++qrr7jyf//9lxkYGPDO56FDh7Ju3bpVuY3q/F0T9dE5Wr1ztLy8nPe/QhFnaWnJ3n//fa4sJSWFAWC7du3ixSYkJCgtJzVTMVmMjo5mANjhw4d5MaWlpczExIQNGzaMMcbYX3/9xQCwiRMnqrzwoljv999/X+nc1VZ0G1oJxhgOHDiAwYMHgzGGe/fucZOvry8KCwuRnp7OxU+YMAEikYh77eXlBeD5LWEAyMjIQHZ2NsaMGYP79+9z6yopKcGAAQPw22+/QS6X8+rwwQcf8F4fP34c5eXl+Oijj3jlH3/8caX6jxgxAhKJBLt27eLKfvnlF9y7dw9jx45V+/0YPnw4TE1NudcuLi4AgLFjx0JfX59XXlZWhlu3bnFlBgYG3O/FxcW4d+8evLy88PjxY1y+fBlAzd6fxiwvLw8ZGRl477330Lx5c668e/fuePPNN3H48OFKy7x4PsXFxUEul2PRokXQ0+N/DAgEAgDPn7l5+PAhRo8ezfsbEAqFcHFx4W6dGRgYQCQSITk5uVZukVVsICEUCtG7d28wxhASEsKVm5mZoVOnTtzfmKLsn3/+wdmzZ5WuV92/a1JzdI4qP0eFQiH3v0Iul+PBgwcoLy9H7969eefe/v37YWpqijfffJO3X05OTjA2NlZ625q8mr///hsA0KlTJ165SCTCa6+9xs23s7NDaGgovv/+e5ibm8PX1xfr16/nPa84cuRIeHh4YOLEibC0tMSoUaOwb98+rf4/Rq2hlbh79y4ePnyITZs2YdOmTUpj7ty5g2bNmgEA2rZty5unKFd8KGVnZwMAxo8fr3KbhYWF3HLA8xOyIsWJam9vzytv3rw5bzng+YfU4MGDER0djWXLlgEAdu3ahdatW+ONN95QWQdVXtw/ReJoY2OjtLzih/HFixexYMECnDhxAkVFRbx4xR9XTd6fxkzVhxoAdOnSBb/88gtKSkpgZGTElb94Pl2/fh16enro2rWryu0ojouqc8bExAQAIBaL8fXXX2PWrFmwtLSEq6sr/P39ERwcDCsrK/V2DsrPN4lEAnNz80rl9+/f517PmzcPx44dg7OzM+zt7TFw4ECMGTMGHh4eAKr/d01eHZ2j/5VXPEcBYMeOHYiMjMTly5e55+AA/v5nZ2ejsLAQFhYWSrdP56lmRUZG4r333sNPP/2Eo0ePYvr06YiIiMDp06e5Z/h/++03JCUl4dChQ0hISMDevXvxxhtv4OjRoxAKhZreBbVRsqiEIvsfO3asygSme/fuuHTpEgCoPPCMMd76VqxYgZ49eyqNNTY25r2ueEWuJoKDg7F//36kpqbC0dERP//8Mz766KNK39CrQ9X+vWy/Hz58iH79+sHExARLly5F+/btIZFIkJ6ejnnz5nHvS03eH6KempxPiuPyww8/KP2HWvGq8ieffILBgwcjLi4Ov/zyCxYuXIiIiAicOHECvXr1Umu7ys6rl51rwPMk5MqVK4iPj0dCQgIOHDiADRs2YNGiRfj888+r/XdNNKMxnKM7d+7Ee++9h4CAAMyZMwcWFhYQCoWIiIjA9evXeftlYWHBuztUUcuWLdWqL3k5Rb+gV65cwWuvvcaVl5WVIScnBz4+Prx4R0dHODo6YsGCBUhNTYWHhweioqLwxRdfAAD09PQwYMAADBgwAN988w3Cw8Px2WefISkpqdK6tAEli0q0bNkSTZs2hUwmq/KgKpLFl2nfvj2A599ya3qSKE7ka9eu8b6B3r9/X+ltlUGDBqFly5bYtWsXXFxc8PjxY4wbN65G266p5ORk3L9/H7Gxsejbty9XnpOTw4urjfenMan4ofaiy5cvw9zcnHfFRpn27dtDLpfj0qVLKhN0xXGxsLCo1nFp3749Zs2ahVmzZiE7Oxs9e/ZEZGQkdu7c+dJla4uRkRFGjhyJkSNHoqysDIGBgfjyyy8RFhZW7b9r8uroHFUuJiYGr732GmJjY7lb6QCwePHiSvU8duwYPDw8XvnCAakeHx8fiEQifPvttxg0aBB3fLZs2YLCwkL4+fkBAIqKimBoaMj7IuLo6Ag9PT2UlpYCAB48eMB7/AIAdw4rYrQNPbOohFAoxLBhw3DgwAFkZmZWmn/37l211ufk5IT27dtj5cqVePToUY3WN2DAAOjr6+O7777jla9bt05pvL6+PkaPHo19+/Zh+/btcHR0rPerJopv2hW/WZeVlWHDhg28uNp4fxoTa2tr9OzZEzt27OD1/5WZmYmjR4/i7bfffuk6AgICoKenh6VLl1Z6jkZxvHx9fWFiYoLw8HDe7TIFxXF5/Pgxnj59ypvXvn17NG3atF4/GF+83ScSidC1a1cwxvDs2bNa/7smqtE5qpyyz8QzZ85AKpXy4kaMGAGZTMY9RlRReXl5pX7/yKtr2bIlwsLCkJCQgEGDBmH9+vWYPn06Pv74Y/Tp04d73v/EiROwtbXFzJkz8d1332Ht2rUYMGAA9/kCAEuXLsXrr7+OhQsX4vvvv0d4eDgmT56MNm3a8LqF0iZ0ZVGFr776CklJSXBxccGkSZPQtWtXPHjwAOnp6Th27Fil/gSroqenh++//x5vvfUWunXrhgkTJqB169a4desWkpKSYGJigoMHD1a5DktLS8yYMYPrr2nQoEH4448/cOTIEZibm/O+pSoEBwfj22+/RVJSEr7++mu134NX5e7ujmbNmmH8+PGYPn06BAIBfvjhB94HJVA7709js2LFCrz11ltwc3NDSEgInjx5grVr18LU1BRLlix56fL29vb47LPPsGzZMnh5eSEwMBBisRhnz55Fq1atEBERARMTE3z33XcYN24cXn/9dYwaNQotW7bEjRs3cOjQIXh4eGDdunW4evUqBgwYgBEjRqBr167Q19fHjz/+iIKCAowaNaru34z/N3DgQFhZWcHDwwOWlpbIysrCunXr4Ofnh6ZNmwKo3b9rUjU6Ryvz9/dHbGws3nnnHfj5+SEnJwdRUVHo2rUr74tyv379MGXKFERERCAjIwMDBw5EkyZNkJ2djf3792PNmjUICgqqt3o3FkuWLEHLli2xbt06zJw5E82bN8fkyZMRHh6OJk2aAAB69OgBX19fHDx4ELdu3YKhoSF69OiBI0eOwNXVFQAwZMgQ5ObmYuvWrbh37x7Mzc3Rr18/fP7557zGolql/htga4+CggI2depUZmNjw5o0acKsrKzYgAED2KZNmxhjyruLYOy/Lme2bdvGKz9//jwLDAxkLVq0YGKxmLVr146NGDGCHT9+nItRdM2grH+m8vJytnDhQmZlZcUMDAzYG2+8wbKysliLFi3YBx98oHQfunXrxvT09Ng///yj9v4r9uPF7iZU7beiu4CzZ89yZadOnWKurq7MwMCAtWrVis2dO5f98ssvDABLSkriLV+d96cxUvV+Hzt2jHl4eDADAwNmYmLCBg8ezC5dusSLqep8YoyxrVu3sl69ejGxWMyaNWvG+vXrxxITEytt39fXl5mamjKJRMLat2/P3nvvPXbu3DnGGGP37t1jU6dOZZ07d2ZGRkbM1NSUubi4sH379qm1n6rqOn78eGZkZFQpvl+/fryucjZu3Mj69u3LnT/t27dnc+bMYYWFhbzlXvZ3TdRH52j1zlG5XM7Cw8NZu3btmFgsZr169WLx8fFs/PjxrF27dpWW37RpE3NycmIGBgasadOmzNHRkc2dO5fdvn1brXoT8qoEjL1wmYdolYcPH6JZs2b44osv8Nlnn1Wa36tXLzRv3hzHjx/XQO0IIYQQou3omUUt8uTJk0plq1evBgBueK2Kzp07h4yMDAQHB9dxzQghhBCiq+jKohbZvn07tm/fjrfffhvGxsY4efIkdu/ejYEDB+KXX37h4jIzM5GWlobIyEjcu3cPf/31FyQSCTdfJpO99GF+Y2Nj6q6G1IpHjx4pbbhUUcuWLbWy7zGiG+gcJaRq1MBFi3Tv3h36+vpYvnw5ioqKuEYvin6dFGJiYrB06VJ06tQJu3fv5iWKAHDz5s1KHeC+aPHixdV6CJ2Ql1m5ciU+//zzKmNycnJga2tbPxUi5AV0jhJSNbqy2Ag9ffoUJ0+erDLmtdde43VMSkhN/fXXX7whz5Tx9PSs9KWGkPpC5yghVaNkkRBCCCGEqES3oSuQy+W4ffs2mjZtqrTfQqIZjDEUFxejVatWNRquUFvQ+dcwNZbzrzroHG2YGvo5SudNw1Xdc4eSxQpu374NGxsbTVeDqHDz5k20adNG09WoM3T+NWy6fv5VB52jDVtDPUfpvGn4XnbuULJYgWKUh5s3b8LExETDtSEKRUVFsLGx4Y6PrqLzr2FqLOdfddA52jA19HOUzpuGq7rnDiWLFSguj5uYmNAJ3QDp+u0LOv8aNl0//6qDztGGraGeo3TeNHwvO3ca3sMNhBBCCCGkwaBkkRBCCCGEqETJIiGEEEIIUYmSRUIIIYQQohIli4QQQgghRCVKFgkhhBBCiEqULBJCCCGEEJUoWSSEEEIIISpRskgIIYQQQlSiZJEQQgghhKhEw/3pIJlMhpSUFOTl5cHa2hpeXl4QCoWarhYhhDQI9BlJaqIxnzd0ZVHHxMbGwt7eHt7e3hgzZgy8vb1hb2+P2NhYTVeNEEI0jj4jSU009vOGkkUdEhsbi6CgIDg6OkIqlaK4uBhSqRSOjo4ICgpqNCc1IYQoQ5+RpCbovAEEjDGm6Uo0FEVFRTA1NUVhYSFMTEw0XR21yGQy2Nvbw9HREXFxcdDT++97gFwuR0BAADIzM5Gdna11l821+bioo7Hsp7ah4/IfbX4v6DNScxp6/aqiy+cNUP1jQ88s6oiUlBTk5uZi9+7dvJMZAPT09BAWFgZ3d3ekpKSgf//+mqkk0VmPHz/G5cuXuddPnjxBbm4ubG1tYWBgwJV37twZhoaGmqgiaeToM5LUBJ03z1GyqCPy8vIAAA4ODkrnK8oVcYTUpsuXL8PJyemlcWlpaXj99dfroUaE8NFn5HO//fYbVqxYgbS0NOTl5eHHH39EQEBAlcskJycjNDQUFy9ehI2NDRYsWID33nuvXuqraXTePEfPLOoIa2trAEBmZqbS+YpyRRwhtalz585IS0vjpp07dwIAdu7cySvv3LmzhmtKGiv6jHyupKQEPXr0wPr166sVn5OTAz8/P3h7eyMjIwOffPIJJk6ciF9++aWOa9ow0HnzHD2zWAE9V9EwafNxUYcu7Wd6ejqcnJx04kqiLh2XV6XN7wV9RlYmEAheemVx3rx5OHToEC9ZGjVqFB4+fIiEhASly5SWlqK0tJRXPxsbGzpvGqDqnjt0ZVFHCIVCREZGIj4+HgEBAbwWWwEBAYiPj8fKlSu18mQmhJBXRZ+RNSOVSuHj48Mr8/X1hVQqVblMREQETE1NucnGxqauq1ln6Lx5jpJFHRIYGIiYmBhcuHAB7u7uMDExgbu7OzIzMxETE4PAwEBNV5EQQjSGPiPVl5+fD0tLS16ZpaUlioqK8OTJE6XLhIWFobCwkJtu3rxZH1WtM3TeUAMXnRMYGIihQ4c22l7mCSGkKvQZWffEYjHEYrGmq1GrGvt5Q8miDhIKhTrdhJ8QQl4FfUZWn5WVFQoKCnhlBQUFMDEx4XWL1Rg05vOGbkMTnbR+/XrY2tpCIpHAxcUFv//+e5Xx+/fvR+fOnSGRSODo6IjDhw/z5jPGsGjRIlhbW8PAwAA+Pj7Izs7mxaSnp+PNN9+EmZkZWrRogcmTJ+PRo0e1vm+EEFJf3NzccPz4cV5ZYmIi3NzcNFQjogmULBKds3fvXoSGhmLx4sVIT09Hjx494Ovrizt37iiNT01NxejRoxESEoLz588jICCAa+GmsHz5cnz77beIiorCmTNnYGRkBF9fXzx9+hQAcPv2bfj4+MDe3h5nzpxBQkICLl682Gj6IiOEaIdHjx4hIyMDGRkZAJ53jZORkYEbN24AeP68YXBwMBf/wQcf4K+//sLcuXNx+fJlbNiwAfv27cPMmTM1UX2iKYxwCgsLGQBWWFio6aqQCtQ9Ls7Ozmzq1Knca5lMxlq1asUiIiKUxo8YMYL5+fnxylxcXNiUKVMYY4zJ5XJmZWXFVqxYwc1/+PAhE4vFbPfu3YwxxjZu3MgsLCyYTCbjYv78808GgGVnZyvd7tOnT1lhYSE33bx5U2fOv7S0NAaApaWlaboqr4w+F/5D70XDpM5xSUpKYgAqTePHj2eMMTZ+/HjWr1+/Ssv07NmTiUQi9tprr7Ft27bVWf1I/arusaFnFolOKSsrQ1paGsLCwrgyPT09+Pj4qOzqQSqVIjQ0lFfm6+uLuLg4AM+/eefn5/O6jzA1NYWLiwukUilGjRqF0tJSiEQiXh9ciud5Tp48CXt7+0rbjYiIwOeff17jfSWEEHX1798frIrulbdv3650mfPnz9dhrUhDR7ehiU65d+8eZDKZ0q4e8vPzlS6jqmsIRbziZ1Uxb7zxBvLz87FixQqUlZXh33//xaeffgpA9TBQuta9BCGEEN1EySIhtaBbt27YsWMHIiMjYWhoCCsrK9jZ2cHS0rLS4PMKYrEYJiYmvIkQQghpaChZ1EEymQzJycnYvXs3kpOTIZPJNF2lemNubg6hUKi0qwcrKyuly6jqGkIRr/j5snWOGTMG+fn5uHXrFu7fv48lS5bg7t27eO211155vwghhBBNoWRRx8TGxsLe3h7e3t4YM2YMvL29YW9vj9jYWE1XrV6IRCI4OTnxunqQy+U4fvy4yq4eXtY1hJ2dHaysrHgxRUVFOHPmjNJ1WlpawtjYGHv37oVEIsGbb75ZG7tGCCGEaESNksXa7MPu2bNnmDdvHhwdHWFkZIRWrVohODgYt2/f5q3D1tYWAoGAN3311Ve8mD///BNeXl6QSCSwsbHB8uXLa7J7Wis2NhZBQUFwdHTkjV/p6OiIoKCgRpMwhoaGYvPmzdixYweysrLw4YcfoqSkBBMmTAAABAcH8xrAzJgxAwkJCYiMjMTly5exZMkSnDt3DtOmTQMACAQCfPLJJ/jiiy/w888/48KFCwgODkarVq0QEBDArWfdunVIT0/H1atXsX79ekybNg0REREwMzOrz90nhBBCape6zaz37NnDRCIR27p1K7t48SKbNGkSMzMzYwUFBUrjT506xYRCIVu+fDm7dOkSW7BgAWvSpAm7cOECY+x5FyQ+Pj5s79697PLly0wqlTJnZ2fm5OTEW0+7du3Y0qVLWV5eHjc9evSI1/zb0tKSvfvuuywzM5Pt3r2bGRgYsI0bN1Z737S5eX95eTmztbVlgwcP5nXfwtjzrmMGDx7M7OzsWHl5uYZqWHM1OS5r165lbdu2ZSKRiDk7O7PTp09z8/r168d1E6Gwb98+1rFjRyYSiVi3bt3YoUOHePPlcjlbuHAhs7S0ZGKxmA0YMIBduXKFFzNu3DjWvHlzJhKJWPfu3dn//ve/Ot/Phoq6ztFN9F40TA39uDT0+jVm1T02aieLtd2HnTK///47A8D+/vtvrqxdu3Zs1apVKpfZsGEDa9asGSstLeXK5s2bxzp16vSyXeJo8wmt6DtLKpUqnZ+amsoAsKSkpPqtWC3Q5uOiDl3aT0oWdRO9Fw1TQz8uDb1+jVl1j41at6EVfdhV7G+uOn3YVYwHnvdhpyoeAAoLCyEQCCrdvvvqq6/QokUL9OrVCytWrEB5eTlvO3379oVIJOJt58qVK/j333+Vbqe0tBRFRUW8SVspumdxcHBQOl9RrqobF0IIIYQQZdRKFuuiD7sXPX36FPPmzcPo0aN5XYlMnz4de/bsQVJSEqZMmYLw8HDMnTv3pdtRzFMmIiICpqam3GRjY6Nizxs+a2trAOANUVeRolwRRwghhBBSHQ2qNfSzZ88wYsQIMMbw3Xff8eaFhoaif//+6N69Oz744ANERkZi7dq1KC0trfH2dKlTZC8vL9ja2iI8PBxyuZw3Ty6XIyIiAnZ2dvDy8tJQDQkhhBCijdRKFuuiDzsFRaL4999/IzEx8aUdFLu4uKC8vBy5ublVbkcxTxld6hRZKBQiMjIS8fHxCAgI4LWGDggIQHx8PFauXAmhUKjpqhJCCCFEi6iVLNZFH3bAf4lidnY2jh07hhYtWry0LhkZGdDT04OFhQW3nd9++w3Pnj3jbadTp05o1qyZOruptQIDAxETE4MLFy7A3d0dJiYmcHd3R2ZmJmJiYhAYGKjpKhLSKNRm92IAKnUbpphWrFjBxTx48ADvvvsuTExMYGZmhpCQEDx69Ii3nsbevRghpIbUbTmzZ88eJhaL2fbt29mlS5fY5MmTmZmZGcvPz2eMPe8+5NNPP+XiT506xfT19dnKlStZVlYWW7x4Ma/rnLKyMjZkyBDWpk0blpGRwesaR9GyOTU1la1atYplZGSw69evs507d7KWLVuy4OBgbjsPHz5klpaWbNy4cSwzM5Pt2bOHGRoaNpqucyoqLy9nSUlJLDo6miUlJWlldzkV6cpxeRld2s/G3Bq6trsXY4zxPhfz8vLY1q1bmUAgYNevX+diBg0axHr06MFOnz7NUlJSmL29PRs9ejRvPxpz92K6rKEfl4Zev8aszrrOYax2+7DLyclhAJROim5e0tLSmIuLCzM1NWUSiYR16dKFhYeHs6dPn/K288cffzBPT08mFotZ69at2VdffaXWftEJ3TA1luOiS/vZmJPF+uhebOjQoeyNN97gXl+6dIkBYGfPnuXKjhw5wgQCAbt16xZjrGbdiz19+pQVFhZy082bN3XmHNUlDf2zo6HXrzGr02RRV9EJ3TA1luOiS/vZWJPF0tJSJhQK2Y8//sgrDw4OZkOGDFG6jI2NTaU+ZBctWsS6d++uND4/P5/p6+uzXbt2cWVbtmxhZmZmvLhnz54xoVDIYmNjGWPP7/oMHTqUF3PixAkGgD148EDpthYvXqz0i7wunKO6pKF/djT0+jVmddLPItEOMpkMycnJ2L17N5KTkyGTyTRdJUIahfroXmzHjh1o2rQp7xnk/Px87vltBX19fTRv3pxbT026F9OlHiMIITWnr+kKkNoVGxuLWbNmca3EgefjakdGRlIDF0J0wNatW/Huu+9CIpHU+bbEYjHEYnGdb4cQ0rDRlUUdEhsbi6CgIKVdCAUFBSE2NlZDNSOkcajL7sUAICUlBVeuXMHEiRMrrePOnTu8svLycjx48IBbT026FyOEEICSRZ0hk8nw4YcfgjGmtFNuxhg+/PBDuiVNSB2qq+7FFLZs2QInJyf06NGj0joePnyItLQ0ruzEiROQy+VwcXHhYhp792KEkJqhZFFHJCcnc1cWXhzVRvH6zp07SE5Oru+qEdKohIaGYvPmzdixYweysrLw4YcfoqSkBBMmTAAABAcHIywsjIufMWMGEhISEBkZicuXL2PJkiU4d+4cpk2bxltvUVER9u/fX+mqIgB06dIFgwYNwqRJk/D777/j1KlTmDZtGkaNGoVWrVoBAMaMGQORSISQkBBcvHgRe/fuxZo1axAaGlqH7wYhRBfQM4s64sSJE9zvenp6vKuLFV+fOHECAwYMqPf6EdJYjBw5Enfv3sWiRYuQn5+Pnj17IiEhgWtMcuPGDejp/fc93d3dHdHR0ViwYAHmz5+PDh06IC4uDg4ODrz17tmzB4wxjB49Wul2d+3ahWnTpmHAgAHQ09PDsGHD8O2333LzTU1NcfToUUydOhVOTk4wNzfHokWLMHny5Dp4FwghuoSSRR2Rk5PD/a7sNrSyOEJI3Zg2bVqlK4MKyq7uDx8+HMOHD69ynZMnT64ysWvevDmio6OrXEf37t2RkpJSZQwhhLyIbkPriBcfbu/Tpw8+//xz9OnTp8o4QgghhJCq0JVFHVFxDFiJRIKzZ8/i7Nmz3OunT59WiiOEEEIIeRm6sqgjrl+/zv3+YgOXsrIypXGEEEIIIS9DVxZ1EGOM9/rFZxgJIYQQoh6ZTIaUlBTk5eXB2toaXl5eEAqFmq5WvaArizrixWG8XjWOEEIIIc/FxsbC3t4e3t7eGDNmDLy9vWFvb99oBrugZFFHzJgxo1bjCCGEEPLf6GiOjo6QSqUoLi6GVCqFo6NjoxkdjZJFHVFSUlKrcYQQQkhjJ5PJMGvWLPj7+yMuLg6urq4wNjaGq6sr4uLi4O/vj9mzZ+v86GiULOqI6g7XRcN6EUIIIdWTkpKC3NxczJ8/n9eZPvB8wIuwsDDk5OTofP+llCzqiNOnT3O/m5ubo3379rC2tkb79u1hbm6uNI4QQgghquXl5QFApRGVFBTlijhdRa2hdcQff/wBABCLxTAyMuJ1kWNra4vi4mKUlpZycYTUhuzsbBQXF1cqz8rK4v1UpmnTpujQoUOd1Y0QQl6VtbU1ACAzMxOurq6V5mdmZvLidBUlizpC8Q+7tLQU3bp1Q+/evfHvv/+iWbNmePLkCXJzc3lxhLyq7OxsdOzYscqYsWPHVjn/6tWrlDASQhosLy8v2NraIjw8HHFxcbxb0XK5HBEREbCzs4OXl5cGa1n3KFnUEQ4ODsjMzIRQKMThw4crzRcKhZDJZCovpROiLsUXj507d6JLly68eYovKLa2tjAwMKi0bFZWFsaOHUtfXgghDZpQKERkZCSCgoIQEBCAsLAw7v9tREQE4uPjERMTo/P9LVKyqCMmTJiAPXv2qGyRpSifMGFCfVaLNAJdunTB66+/Xqncw8NDA7UhhJDaFRgYiJiYGMyaNQvu7u5cuZ2dHWJiYhAYGKjB2tUPShZ1RHUvgev6pXJCCCGktgUGBmLo0KGNdgQXShZ1xPr166sdN3v27DquDSGEEKJbhEIh+vfvr+lqaAR1naMj4uLiuN/FYjFvnkQiURpHCCGEEPIylCzqmNatW8PCwoJX1rJlS7Rq1UpDNSKEEEKINqNkUUcoWjnfunULN2/e5M27efMmbt++zYsjhBBCCKkOShZ1hJubW63GEUIIIYQAlCzqDMWVw9qKI4QQQggBKFnUGTExMbUaRwghhBACULKoM+jKIiGEEELqAiWLOsLS0rJW4wghhBBCAEoWdUZ1u8ahLnQIIYQQog5KFnXE9evXazWOEEIIIQSgZFFn6OlV71BWN44QQgghBKBkUWcwxmo1jhBCCCEEoGRRZ9y5c6dW4wghhBBCAEoWdcaTJ09qNY4QQgghBAD0NV0BUjuePXtWq3GEVIeVsQAGD68Ct9X73mnw8CqsjAV1VCtCCCG1iZJFHSGTyWo1jpDqmOIkQpffpgC/qbdcl/9flhBCSMNHySIhpMY2ppVh5KLt6NK5s1rLZV2+jI2RYzCkjupFCCG1TSaTISUlBXl5ebC2toaXlxeEQqGmq1UvavTM4vr162FrawuJRAIXFxf8/vvvVcbv378fnTt3hkQigaOjIw4fPszNe/bsGebNmwdHR0cYGRmhVatWCA4O5g1Ll5ubi5CQENjZ2cHAwADt27fH4sWLUVZWxosRCASVptOnT9dkF7UOdZ1DNCH/EcMTs45Aq55qTU/MOiL/EbXMJ4Roh9jYWNjb28Pb2xtjxoyBt7c37O3tERsbq+mq1Qu1M4e9e/ciNDQUixcvRnp6Onr06AFfX1+VrWxTU1MxevRohISE4Pz58wgICEBAQAAyMzMBAI8fP0Z6ejoWLlyI9PR0xMbG4sqVKxgy5L9rDpcvX4ZcLsfGjRtx8eJFrFq1ClFRUZg/f36l7R07dgx5eXnc5OTkpO4uaiWRqHq39KobRwghhJDniWJQUBAcHR0hlUpRXFwMqVQKR0dHBAUFNY6EkanJ2dmZTZ06lXstk8lYq1atWEREhNL4ESNGMD8/P16Zi4sLmzJlispt/P777wwA+/vvv1XGLF++nNnZ2XGvc3JyGAB2/vz5au5JZYWFhQwAKywsrPE6NEUgEDAAL50EAoGmq6o2bT4u6tC2/UxLS2MAWFpaWr0uW9+07bjUJXovGqaGflwaev2qUl5ezmxtbdngwYOZTCbjzZPJZGzw4MHMzs6OlZeXa6iGr6a6x0atK4tlZWVIS0uDj48PV6anpwcfHx9IpVKly0ilUl48APj6+qqMB4DCwkIIBAKYmZlVGdO8efNK5UOGDIGFhQU8PT3x888/V7k/paWlKCoq4k3aSiCoXsvS6sYRQgghjV1KSgpyc3Mxf/78So9x6enpISwsDDk5OUhJSdFQDeuHWsnivXv3IJPJYGlpySu3tLREfn6+0mXy8/PVin/69CnmzZuH0aNHw8TERGnMtWvXsHbtWkyZMoUrMzY2RmRkJPbv349Dhw7B09MTAQEBVSaMERERMDU15SYbGxuVsYQQQghpXPLy8gAADg4OSucryhVxuqpBtYZ+9uwZRowYAcYYvvvuO6Uxt27dwqBBgzB8+HBMmjSJKzc3N0doaCj3uk+fPrh9+zZWrFjBe/6xorCwMN4yRUVFlDASQgghBABgbW0NAMjMzISrq2ul+Yr2F4o4XaXWlUVzc3MIhUIUFBTwygsKCmBlZaV0GSsrq2rFKxLFv//+G4mJiUqvKt6+fRve3t5wd3fHpk2bXlpfFxcXXLt2TeV8sVgMExMT3qSt5HJ5rcYRQgghjZ2XlxdsbW0RHh5e6f+nXC5HREQE7Ozs4OXlpaEa1g+1kkWRSAQnJyccP36cK5PL5Th+/Djc3NyULuPm5saLB4DExERevCJRzM7OxrFjx9CiRYtK67l16xb69+8PJycnbNu2rVpdwGRkZOh8tq9AzywSQgghtUsoFCIyMhLx8fEICAjgtYYOCAhAfHw8Vq5cqfP9LarddU5oaCg2b96MHTt2ICsrCx9++CFKSkowYcIEAEBwcDDCwsK4+BkzZiAhIQGRkZG4fPkylixZgnPnzmHatGkAnieKQUFBOHfuHHbt2gWZTIb8/Hzk5+dz/SgqEsW2bdti5cqVuHv3LhejsGPHDuzevRuXL1/G5cuXER4ejq1bt+Ljjz9+pTdIW1DXOYQQQqpD3b6SV69ejU6dOsHAwAA2NjaYOXMmnj59Wk+11bzAwEDExMTgwoULcHd3h4mJCdzd3ZGZmYmYmBgEBgZquop1ryZNrdeuXcvatm3LRCIRc3Z2ZqdPn+bm9evXj40fP54Xv2/fPtaxY0cmEolYt27d2KFDh7h5ii5vlE1JSUmMMca2bdumMkZh+/btrEuXLszQ0JCZmJgwZ2dntn//frX2S5ub96t6f6p6z7SFNh8XdWjbflLXOaqtW7eOtWvXjonFYubs7MzOnDlTZfy+fftYp06dmFgsZg4ODrzPSIVLly6xwYMHMxMTE2ZoaMh69+7NdS9W1efovn37uHUom7979+5q75e2naONhTrHZc+ePUwkErGtW7eyixcvskmTJjEzMzNWUFCgNH7Xrl1MLBazXbt2sZycHPbLL78wa2trNnPmzDqpX0NWXl7OkpKSWHR0NEtKStLa7nIqqu6x0b7MoQ5p8wlNyaL207b9pGRROXX/GZ86dYoJhUK2fPlydunSJbZgwQLWpEkTduHCBS7m2rVrrHnz5mzOnDksPT2dXbt2jf3000/cOsvLy1leXh5v+vzzz5mxsTErLi7m1gOAbdu2jRf35MmTOnsvSP1Q57io21fy1KlT2RtvvMErCw0NZR4eHiq38fTpU1ZYWMhNN2/epPOmgaqTfhYJIYRU7ZtvvsGkSZMwYcIEdO3aFVFRUTA0NMTWrVuVxq9ZswaDBg3CnDlz0KVLFyxbtgyvv/461q1bx8V89tlnePvtt7F8+XL06tUL7du35/qUBZ4/V2VlZcWbfvzxR4wYMQLGxsa87ZmZmfHiJBJJ3b0ZpEGpSV/J7u7uSEtL425V//XXXzh8+DDefvttlduhbul0DyWLOqJZs2a812KxGMbGxhCLxVXG6araHL8cABhjWLRoEaytrWFgYAAfHx9kZ2fzYq5evYqhQ4fC3NwcJiYm8PT0RFJSUq3vW0Px+PFjAEB6enql6dSpU9i1axdOnTqldH5WVpaGa1836mLgArlcjkOHDqFjx47w9fWFhYUFXFxcEBcXp7IeaWlpyMjIQEhISKV5U6dOhbm5OZydnbF161YwpnqMbl0auIDUrK/kMWPGYOnSpfD09ESTJk3Qvn179O/fX+lwuwphYWEoLCzkpps3b9bqfhANqJfrnFpCm2+xvPPOO9W6Bf3OO+9ouqpqawi3Ab/66itmamrK4uLi2B9//MGGDBnC7OzseLfwOnTowN5++232xx9/sKtXr7KPPvqIGRoasry8vDrZT03bvHmzWo8/KJuuXr2q6d14KXWOy61btxgAlpqayiufM2cOc3Z2VrpMkyZNWHR0NK9s/fr1zMLCgjHGWF5eHgPADA0N2TfffMPOnz/PIiIimEAgYMnJyUrX+eGHH7IuXbpUKl+6dCk7efIkS09PZ1999RUTi8VszZo1Kvdn8eLFSo+btpyjjUV1z9GanJ9JSUnM0tKSbd68mf35558sNjaW2djYsKVLl9Z6/Uj9o2cWa0CbT+gBAwZU65/zgAEDNF1Vtal7XGp7/HK5XM6srKzYihUruPkPHz5kYrGYaxxw9+5dBoD99ttvXExRUREDwBITE+tkPzXt7t27bPPmzSwlJYWlpaXxpp07dzIAbOfOnZXmKSZtSBQZ03yyqFjn6NGjeTGDBw9mo0aNqrS+x48fM1NTU7Zy5cqX1nfhwoWsTZs2KufTs2faobrnaGlpKRMKhezHH3/klQcHB7MhQ4YoXcbT05PNnj2bV/bDDz8wAwODSmMlv2r9SP2r7rFpUCO4kJp78bmkV43TVorbgBW7b6rObcCKI/kAz28DKm7z5eTkID8/n3er0NTUFC4uLpBKpRg1ahRatGiBTp064X//+x9ef/11iMVibNy4ERYWFnByclK63dLSUpSWlnKvte0Wn7m5OSZOnFhlTJcuXfD666/XU400ry4GLjA3N4e+vj66du3Ki+nSpQtOnjxZaX0xMTF4/PgxgoODX1pfFxcXLFu2DKWlpZUeWQGeP86irJxop4p9JQcEBAD4r69kRXd2L3r8+HGlfo0VfQqyKh5hILqFnlnUERV7j9fX538HqPha13uZr4vxyxU/q4oRCAQ4duwYzp8/j6ZNm0IikeCbb75BQkKCyudE6SFw3VMXAxeIRCL06dMHV65c4cVcvXoV7dq1q7S+LVu2YMiQIWjZsuVL65uRkYFmzZpRQtiIqNtX8uDBg/Hdd99hz549yMnJQWJiIhYuXIjBgwfrfEfU5D90ZVFHhISEYO7cuUqH81OM2qKnp6f0gXfy6hhjmDp1KiwsLJCSkgIDAwN8//33GDx4MM6ePat0JCEam1w3hYaGYvz48ejduzecnZ2xevXqSv+MW7dujYiICADPBy7o168fIiMj4efnhz179uDcuXO8IU3nzJmDkSNHom/fvvD29kZCQgIOHjyI5ORk3ravXbuG3377rVIDLQA4ePAgCgoK4OrqColEgsTERISHh2P27Nl192aQBmfkyJG4e/cuFi1ahPz8fPTs2RMJCQncl+EbN27wriQuWLAAAoEACxYswK1bt9CyZUsMHjwYX375paZ2gWgAJYs64q+//uISxfLyct68Z8+eAXh+heOvv/7S6duCdXEbUPGzoKCAl/QVFBSgZ8+eAIATJ04gPj4e//77LzfG+IYNG5CYmIgdO3bg008/rbRdusWnm9T9Z+zu7o7o6GgsWLAA8+fPR4cOHRAXFwcHBwcu5p133kFUVBQiIiIwffp0dOrUCQcOHICnpydv21u3bkWbNm0wcODASvVq0qQJ1q9fj5kzZ4IxBnt7e66bn8bg8ePHuHz5Mvf6yZMnyM3Nha2tLQwMDLjyzp07w9DQUBNVrDfTpk1Tedv5xS8g+vr6WLx4MRYvXlwPNSMNVn08QKkttPkh3JKSEpaWlsaCg4OZUCjkNWoRCoUsODiYpaWlsZKSEk1XVW01aeAybdo07rVMJmOtW7eusoGLv78/r8zNza1SA5eKDQYKCwt5DVx+/vlnpqenx+sAmTHGOnbsyL788stq1Vubz78XaVOn2y+jS8flVWnze6E4J182aeM529CPS0OvX2NGDVwaGUNDQ7z++uvYsWMHNm/ejLCwMHzzzTcIDQ1FREREoxoTurZvAwoEAnzyySf44osv0KFDB9jZ2WHhwoVo1aoV95C4m5sbmjVrhvHjx2PRokUwMDDA5s2bkZOTAz8/P428D4SQ/3Tu3BlpaWnc66ysLIwdOxY7d+5Ely5deHGEED5KFnWQSCTCu+++i2+++Qbvvvtuo0oUgbq5DTh37lyUlJRg8uTJePjwITw9PZGQkMCNfmFubo6EhAR89tlneOONN/Ds2TN069YNP/30E3r06FG/bwAhpBLFF+oXNbYW+4TUBCWLRCep80wOAAwfPhzDhw9XuT6BQIClS5di6dKlKmN69+6NX375Re26EkIIIQ0ZdZ1DCCGEEEJUomSREEIIIYSoRMkiIYQQQghRiZJFQgghhBCiEiWLhBBCCCFEJUoWCSGEEEKISpQsEkIIIYQQlaifRS2XnZ2N4uLiSuVZWVm8n8o0bdoUHTp0qLO6EUIIIUT7UbKoxbKzs9GxY8cqY8aOHVvl/KtXr1LCSAghhBCVKFnUYoorii+ObQoAT548QW5uLmxtbWFgYFBpWcW4qMquShJCCCGEKFCyqANUjW3q4eGhgdoQQgghRJdQAxdCCCGEEKISJYuEEEIIIUQlug2t5ayMBTB4eBW4rV7eb/DwKqyMBXVUK0IIIYToCkoWtdwUJxG6/DYF+E295br8/7KEEEIIIVWhZFHLbUwrw8hF29Glc2e1lsu6fBkbI8dgSB3VixBCCCG6gZJFLfb48WPkP2I49dcjPDGT8+a9tOucPBnyH7H6qiohhBBCtBQli1rs8uXLAIBJkybVeB1NmzatreoQQgghRAdRsqjFAgICAACdO3eGoaEhb56i021lHXYr0HB/hBBCSPXIZDKkpKQgLy8P1tbW8PLyglAo1HS16gUli1rM3NwcEydOrDJGVYfdhBBCCKme2NhYzJo1C7m5uVyZra0tIiMjERgYqLmK1RPqZ5EQQgghRIXY2FgEBQXB0dERUqkUxcXFkEqlcHR0RFBQEGJjYzVdxTpHySIhhBBCiBIymQyzZs2Cv78/4uLi4OrqCmNjY7i6uiIuLg7+/v6YPXs2ZDKZpqtap+g2NCGEEJ2UnZ2N4uJipfOysrJ4P5Wh57pJSkoKcnNzsXv3bujp8a+v6enpISwsDO7u7khJSUH//v01U8l6QMkiIYQQnZOdnY2OHTu+NG7s2LFVzr969SoljI1YXl4eAMDBwUHpfEW5Ik5XUbJICCFE5yiuKKrqEeKlfdH+f48Sqq5MksbB2toaAJCZmQlXV9dK8zMzM3lxuoqSRUIIITqrqh4hPDw86rk2RNt4eXnB1tYW4eHhiIuL492KlsvliIiIgJ2dHby8vDRYy7pHDVx00KNHjxAaGgoACA0NxaNHjzRcI0IIIUT7CIVCREZGIj4+HgEBAbzW0AEBAYiPj8fKlSt1vr/FGiWL69evh62tLSQSCVxcXPD7779XGb9//3507twZEokEjo6OOHz4MDfv2bNnmDdvHhwdHWFkZIRWrVohODgYt2/f5q3jwYMHePfdd2FiYgIzMzOEhIRUSoL+/PNPeHl5QSKRwMbGBsuXL6/J7mk1Z2dnNG3aFL/++isA4Ndff0XTpk3h7Oys4ZoRQggh2icwMBAxMTG4cOEC3N3dYWJiAnd3d2RmZiImJob6WVRm7969CA0NxeLFi5Geno4ePXrA19cXd+7cURqfmpqK0aNHIyQkBOfPn0dAQAACAgK4+/yPHz9Geno6Fi5ciPT0dMTGxuLKlSsYMmQIbz3vvvsuLl68iMTERMTHx+O3337D5MmTuflFRUUYOHAg2rVrh7S0NKxYsQJLlizBpk2b1N1FreXs7IyzZ88qnXf27FlKGAkhhJAaCAwMxLVr15CUlITo6GgkJSUhOzu7USSKAACmJmdnZzZ16lTutUwmY61atWIRERFK40eMGMH8/Px4ZS4uLmzKlCkqt/H7778zAOzvv/9mjDF26dIlBoCdPXuWizly5AgTCATs1q1bjDHGNmzYwJo1a8ZKS0u5mHnz5rFOnTpVe98KCwsZAFZYWFjtZRqK4uJiBuClU3FxsaarqjZtPi7q0KX9TEtLYwBYWlqapqvyynTpuLwqbXovXvUc1KZzuKEfl4Zev8asusdGrSuLZWVlSEtLg4+PD1emp6cHHx8fSKVSpctIpVJePAD4+vqqjAeAwsJCCAQCmJmZceswMzND7969uRgfHx/o6enhzJkzXEzfvn0hEol427ly5Qr+/fdfpdspLS1FUVERb9JWQ4cO5b3u3r0776eqOEIIIYSQqqiVLN67dw8ymQyWlpa8cktLS+Tn5ytdJj8/X634p0+fYt68eRg9ejRMTEy4dVhYWPDi9PX10bx5c249qrajmKdMREQETE1NucnGxkZpnDY4ceIE7/Wff/7J+6kqjhBCCCGkKg2q65xnz55hxIgRYIzhu+++q/PthYWFca2GgefPPWpzwggARkZG+O233yr1Iebl5YXHjx9runqEEEII0TJqJYvm5uYQCoUoKCjglRcUFMDKykrpMlZWVtWKVySKf//9N06cOMFdVVSs48UGNOXl5Xjw4AG3HlXbUcxTRiwWQywWq9pdrVRSUoKuXbtCIpFwfYg9ffqUEkVCCCGE1Ihat6FFIhGcnJxw/Phxrkwul+P48eNwc3NTuoybmxsvHgASExN58YpEMTs7G8eOHUOLFi0qrePhw4dIS0vjyk6cOAG5XA4XFxcu5rfffsOzZ8942+nUqROaNWumzm5qJScnJ+53AwMDjB07Funp6Rg7dixvdIKKcYQQQgghL6N21zmhoaHYvHkzduzYgaysLHz44YcoKSnBhAkTAADBwcEICwvj4mfMmIGEhARERkbi8uXLWLJkCc6dO4dp06YBeJ4oBgUF4dy5c9i1axdkMhny8/ORn5+PsrIyAM974B80aBAmTZqE33//HadOncK0adMwatQotGrVCgAwZswYiEQihISE4OLFi9i7dy/WrFnDu82sy15MyHft2gUnJyfs2rWryjhCCCGEkKqo/cziyJEjcffuXSxatAj5+fno2bMnEhISuMYkN27c4A2H4+7ujujoaCxYsADz589Hhw4dEBcXxw2+fevWLfz8888AgJ49e/K2lZSUhP79+wN4nvxMmzYNAwYMgJ6eHoYNG4Zvv/2WizU1NcXRo0cxdepUODk5wdzcHIsWLeL1xajLTE1N0b59e1y/fl1lTPv27WFqalqPtSKEEEKItqtRA5dp06ZxVwZflJycXKls+PDhGD58uNJ4W1tbMMZeus3mzZsjOjq6ypju3bsjJSXlpevSVdeuXYO9vb3ShLF9+/a4du2aBmpFCCGEEG1GY0PrmGvXruHhw4fw8PCAjY0NPDw88PDhQ0oUSb2RyWQ4d+4cAODcuXOQyWQarlH9q80hURWysrIwZMgQmJqawsjICH369MGNGze4+f3794dAIOBNH3zwAW8dN27cgJ+fHwwNDWFhYYE5c+agvLy8dnaaEB0nk8mQnJyM3bt3Izk5uVF9tlGyqINMTU1x8uRJ3LhxAydPnqRbz6TOKYbtXLlyJWxsbDBlyhQAwJQpU2BjY4OVK1ciPT29UbTKr+0hUQHg+vXr8PT0ROfOnZGcnIw///wTCxcuhEQi4a1r0qRJyMvL46bly5dz82QyGfz8/FBWVobU1FTs2LED27dvx6JFi+rmjSBEh8TGxsLe3h7e3t4YM2YMvL29YW9vj9jYWE1XrX7Uy3gyWoKGJGqYGstx0eb9VAyN9rJJG4ZOe5G6x6UuhkQdOXIkGzt2bJXb7devH5sxY4bK+YcPH2Z6enosPz+fK/vuu++YiYkJb5jUqmjTOUrD/TUcDb1+L3PgwAEmEAjY4MGDmVQqZcXFxUwqlbLBgwczgUDADhw4oOkq1lidDPdHCCHKdOjQAcbGxgCAZs2aYe7cuVi3bh3mzp3LdV1lbGyMDh06aLKada4uhkSVy+U4dOgQOnbsCF9fX1hYWMDFxQVxcXGV1rVr1y6Ym5vDwcEBYWFhvCu5UqkUjo6OvJGufH19UVRUhIsXLyqtmy4NiUpITchkMsyaNQv+/v6Ii4uDq6srjI2N4erqiri4OPj7+2P27Nk6f0uakkVCyCs7ffo0Hj16hGbNmuHOnTv4+uuvMXXqVHz99de4c+cOmjVrhkePHuH06dOarmqdqoshUe/cuYNHjx7hq6++wqBBg3D06FG88847CAwMxK+//sotM2bMGOzcuRNJSUkICwvDDz/8gLFjx750O4p5yujSkKiE1ERKSgpyc3Mxf/58Xk8vwPMvgmFhYcjJydH5xrUNarg/Qoh2+uGHHwAAS5cuhUAgQHJyMvLy8mBtbQ0vLy8sWbIEM2bMwA8//IA333xTw7XVLnK5HAAwdOhQzJw5E8DzbsZSU1MRFRWFfv36AQCvmzBHR0dYW1tjwIABuH79Otq3b1+jbWv7kKhWxgIYPLwK3Fb/uojBw6uwMhbUQa2INsnLywMArru/FynKFXG6ipJFQsgrKy4uBgDcvn0b9vb2yM3N5ebZ2tpi1KhRvDhdVRdDopqbm0NfXx9du3blxXTp0gUnT55UWRfF6FbXrl1D+/btYWVlValVtq4PiTrFSYQuv00BflN/2S7/vzxp3KytrQEAmZmZcHV1rTRf0RBNEaer6DY0IeSVeXl5AXh+29LBwQFSqRTFxcWQSqVwcHDAV199xYvTVXUxJKpIJEKfPn1w5coVXszVq1fRrl07lXXJyMgA8N8/MTc3N1y4cIHXKjsxMREmJiaVElFdsTGtDFl9NwKTf1V7yuq7ERvTyjS9C0TDvLy8YGtri/DwcO4qv4JcLkdERATs7Ox0/rONWkNXoO0ttnRVYzku2ryfjx8/5lo8+/n5sdTUVFZUVMRSU1OZn58fN+/x48earqra1D0ue/bsYWKxmG3fvp1dunSJTZ48mZmZmXGtkMeNG8c+/fRTLv7UqVNMX1+frVy5kmVlZbHFixezJk2asAsXLnAxsbGxrEmTJmzTpk0sOzubrV27lgmFQpaSksIYY+zatWts6dKl7Ny5cywnJ4f99NNP7LXXXmN9+/bl1lFeXs4cHBzYwIEDWUZGBktISGAtW7ZkYWFhdfZeaBK1hm44Gnr9XqZia+iKn22NqTU0JYsVaPsJrasay3HR5v1MSkriEkI9PT1edzlCoZD7PSkpSdNVVVtNjsvatWtZ27ZtmUgkYs7Ozuz06dPcvH79+rHx48fz4vft28c6duzIRCIR69atGzt06FCldW7ZsoXZ29sziUTCevToweLi4rh5N27cYH379mXNmzdnYrGY2dvbszlz5lSqc25uLnvrrbeYgYEBMzc3Z7NmzWLPnj2r9n5p0zlKyWLD0dDrVx0HDhxgtra2vM82Ozs7rU4UGav+saFnFgkhr0zxcPeMGTOwbt26SvNnzJiBNWvW6PxD4Aq1OSSqwvvvv4/3339f6TwbGxtey2hV2rVrp3R0GEJI1QIDAzF06FCkpKTwGu8JhUJNV61eULJICHlliufiRo0aheXLl2PDhg1cK9yPPvoIaWlpWLNmjc4/BE4I0V1CoRD9+/fXdDU0gpJFQsgrq/gQeFxcHD755BNuXqN6CJwQQnQQJYuEkFcmFAoRGRmJoKAgDB06FIMGDYKBgQGePHmChIQEHDp0CDExMY3mlg0hhOgSShYJIbUiMDAQs2fPxqpVqxAfH8+V6+vrY/bs2QgMDNRg7QghhNQUJYuEkFoRGxuLlStXws/PD2+99RZ3ZfHIkSNYuXIlXF1dKWEkhBAtRMkiIeSVyWQyzJo1C/7+/oiLi+ONofrBBx8gICAAs2fPxtChQ+lWNCGEaBlKFgkhrywlJQW5ubnYvXs3GGOVxoYOCwuDu7s7UlJSGm1rQlK/Hj9+DABIT09XOv/JkyfIzc2Fra0tDAwMKs3Pysqq0/oRok0oWSSEvDJF/4nXr1/H6NGjK40N/cUXX/DiCKlrly9fBgBMmjTpldbTtGnT2qgOIVqNkkVCyCtT9J84btw4+Pv7Y/fu3XBwcEBmZibCw8Mxbtw4XhwhdS0gIAAA0LlzZxgaGlaan5WVhbFjx2Lnzp3o0qWL0nU0bdoUHTp0qMtqasT69euxYsUK5Ofno0ePHli7di2cnZ1Vxj98+BCfffYZYmNj8eDBA7Rr1w6rV6/G22+/XY+11jyZTEadchNCSE25u7tDX18fLVq0QGxsLPT1n3+0uLq6IjY2Fm3atMH9+/fh7u6u4ZqSxsLc3BwTJ058aVyXLl3w+uuv10ONGoa9e/ciNDQUUVFRcHFxwerVq+Hr64srV67AwsKiUnxZWRnefPNNWFhYICYmBq1bt8bff/8NMzOz+q+8BsXGxmLWrFmV7ppERkY2ioZ7ei8PIUT7rF+/Hra2tpBIJHBxccHvv/9eZfz+/fvRuXNnSCQSODo6VhoSjTGGRYsWwdraGgYGBvDx8UF2djY3Pzk5GQKBQOl09uzZOtnHhiQ1NRXl5eUoKChAYGAgpFIpiouLIZVKERgYiIKCApSXlyM1NVXTVSWkUfvmm28wadIkTJgwAV27dkVUVBQMDQ2xdetWpfFbt27FgwcPEBcXBw8PD9ja2qJfv37o0aNHPddcc2JjYxEUFARHR0feZ5ujoyOCgoIQGxur6SrWOUoWic5RfHNevHgx0tPT0aNHD/j6+uLOnTtK41NTUzF69GiEhITg/PnzCAgIQEBAADIzM7mY5cuX49tvv0VUVBTOnDkDIyMj+Pr64unTpwCeX1nLy8vjTRMnToSdnR169+5dL/utSYpnEXfu3IkLFy7A3d0dJiYmcHd3R2ZmJnbu3MmLI4TUv7KyMqSlpcHHx4cr09PTg4+PD6RSqdJlfv75Z7i5uWHq1KmwtLSEg4MDwsPDIZPJVG6ntLQURUVFvElbvdjTg6urK4yNjeHq6oq4uDj4+/tj9uzZVb4fOoERTmFhIQPACgsLNV0VUoG6x8XZ2ZlNnTqVey2TyVirVq1YRESE0vgRI0YwPz8/XpmLiwubMmUKY4wxuVzOrKys2IoVK7j5Dx8+ZGKxmO3evVvpOsvKyljLli3Z0qVLq1VnxrT7/EtKSmIAmFQqZeXl5SwpKYlFR0ezpKQkVl5ezlJTUxkAlpSUpOmqqk2bj0tt06X3Ii0tjQFgaWlpmq7KK6vucbl16xYDwFJTU3nlc+bMYc7OzkqX6dSpExOLxez9999n586dY3v27GHNmzdnS5YsUbmdxYsXMwCVJm08b3T5s42x6p87dGWR6JSafHOWSqW8eADw9fXl4nNycpCfn8+LMTU1hYuLS5Xfxu/fv48JEyaorKsuffuuODa0QCBA//79MXr0aPTv3x8CgYDGhiZES8nlclhYWGDTpk1wcnLCyJEj8dlnnyEqKkrlMmFhYSgsLOSmmzdv1mONa1fFnh7s7e3h7e2NMWPGwNvbG/b29vjrr794cbqKkkWiU+7duweZTAZLS0teuaWlJfLz85Uuk5+fX2W84qc669yyZQt8fX3Rpk0blXWNiIiAqakpN9nY2FS9cw2YYmzo+Ph4BAQE8J7rCQgIQHx8PFauXNloWg4S0hCZm5tDKBSioKCAV15QUAArKyuly1hbW6Njx468v90uXbogPz8fZWVlSpcRi8UwMTHhTdpK0YPD2LFjlT6zOHbsWF6crqJkkZBa9s8//+CXX35BSEhIlXG69O0beD42dExMjNJnFmNiYhpFi0FCGjKRSAQnJyccP36cK5PL5Th+/Djc3NyULuPh4YFr165BLpdzZVevXoW1tTVEIlGd11nTFD09WFpaIjY2lvfMYmxsLCwtLaGvr6/zPT1Qskh0Sk2+OVtZWVUZr/hZ3XVu27YNLVq0wJAhQ6qsqy59+1YIDAzEtWvXkJSUhOjoaCQlJSE7O5sSRUIaiNDQUGzevBk7duxAVlYWPvzwQ5SUlHCPzAQHByMsLIyL//DDD/HgwQPMmDEDV69exaFDhxAeHo6pU6dqahfqlaKnhzt37ijt6eHOnTuNoqcHShaJTqnJN2c3NzdePAAkJiZy8XZ2drCysuLFFBUV4cyZM5XWyRjDtm3bEBwcjCZNmtTWbmkVoVDIe2aRbj0T0nCMHDkSK1euxKJFi9CzZ09kZGQgISGBe8zmxo0bvOfvbGxs8Msvv+Ds2bPo3r07pk+fjhkzZuDTTz/V1C7UK8V78cMPPyi9a/LDDz/w4nQVdcpNdE5oaCjGjx+P3r17w9nZGatXr670zbl169aIiIgAAMyYMQP9+vVDZGQk/Pz8sGfPHpw7dw6bNm0CAAgEAnzyySf44osv0KFDB9jZ2WHhwoVo1aoVN0qEwokTJ5CTk1OtzoAJIUQTpk2bhmnTpimdl5ycXKnMzc0Np0+fruNaNUyKZxHbt2+Pa9euVRrBRdGHr64/s0jJItE5I0eOxN27d7Fo0SLk5+ejZ8+elb456+n9d1Hd3d0d0dHRWLBgAebPn48OHTogLi4ODg4OXMzcuXNRUlKCyZMn4+HDh/D09ERCQgIkEglv21u2bIG7uzs6d+5cPztLCCGkzlTs6SEuLg79+/fn5snl8kbT04OAMcY0XYmGoqioCKampigsLNSJ58d0RWM5Lo1lP7UNHZf/6NJ7kZ6eDicnJ6SlpWn9cH8N/bg09Pq9jGIEF39/f4SFhXHj3kdERCA+Pl6rG/BV99jQlUVCCCGEEBUUPT3MmjWL1+rZzs5OqxNFdVCySAghhBBShcDAQAwdOrTSM4uNpQEfJYuEEEIIIS+h6OmhMaKucwghhBBCiEqULBJCCCGEEJUoWSSEEEIIISrRM4uEkFolk8ka7UPghBCii2p0ZXH9+vWwtbWFRCKBi4sL14O5Kvv370fnzp0hkUjg6OiIw4cP8+bHxsZi4MCBaNGiBQQCATIyMnjzc3NzIRAIlE779+/n4pTN37NnT012kRBSA7GxsbC3t4e3tzfGjBkDb29v2NvbIzY2VtNVI4QQUkNqJ4t79+5FaGgoFi9ejPT0dPTo0QO+vr64c+eO0vjU1FSMHj0aISEhOH/+PAICAhAQEIDMzEwupqSkBJ6envj666+VrsPGxgZ5eXm86fPPP4exsTHeeustXuy2bdt4cS8Ox0YIqRuKjmsdHR0hlUpRXFwMqVQKR0dHBAUFUcJICCFaSu1k8ZtvvsGkSZMwYcIEdO3aFVFRUTA0NMTWrVuVxq9ZswaDBg3CnDlz0KVLFyxbtgyvv/461q1bx8WMGzcOixYtgo+Pj9J1CIVCWFlZ8aYff/wRI0aMgLGxMS/WzMyMF/ficGyEkNonk8kwa9Ys+Pv748CBA3j69CkOHjyIp0+f4sCBA/D398fs2bMhk8k0XVVCCCFqUitZLCsrQ1paGi+p09PTg4+PD6RSqdJlpFJppSTQ19dXZXx1pKWlISMjAyEhIZXmTZ06Febm5nB2dsbWrVtR1WiGpaWlKCoq4k2EEPWlpKQgNzcX7u7u6NixI+82dMeOHeHm5oacnBykpKRouqqEEFIjZWVlWL16NT7++GOsXr0aZWVlmq5SvVErWbx37x5kMhksLS155ZaWlsjPz1e6TH5+vlrx1bFlyxZ06dKFN+wOACxduhT79u1DYmIihg0bho8++ghr165VuZ6IiAiYmppyk42NTY3rREhjlpeXBwCYP3++0tvQn332GS+OEEK0ydy5c2FkZISZM2di3bp1mDlzJoyMjDB37lxNV61eaF3XOU+ePEF0dLTSq4oLFy6Eh4cHevXqhXnz5mHu3LlYsWKFynWFhYWhsLCQm27evFmXVSdEZ1lYWAAAPDw8EBcXB1dXVxgbG8PV1RVxcXHw8PDgxRFCiLZQ5BItWrTA5s2bkZeXh82bN6NFixZYsWJFo0gY1UoWzc3NIRQKUVBQwCsvKCiAlZWV0mWsrKzUin+ZmJgYPH78GMHBwS+NdXFxwT///IPS0lKl88ViMUxMTHgTIaT2VfU4CCGENFRlZWVYtWoVLC0t8c8//2DixImwsrLCxIkT8c8//8DS0hKrVq3S+VvSaiWLIpEITk5OOH78OFcml8tx/PhxuLm5KV3Gzc2NFw8AiYmJKuNfZsuWLRgyZAhatmz50tiMjAw0a9YMYrG4RtsihFSPojeEkydPIiAggHcbOiAgAKdOneLFEUKINtiwYQPKy8vxxRdfQF+f3zW1vr4+li5divLycmzYsEFDNawfanfKHRoaivHjx6N3795wdnbG6tWrUVJSggkTJgAAgoOD0bp1a0RERAAAZsyYgX79+iEyMhJ+fn7Ys2cPzp07h02bNnHrfPDgAW7cuIHbt28DAK5cuQIAXItmhWvXruG3336r1E8jABw8eBAFBQVwdXWFRCJBYmIiwsPDMXv2bHV3kRCiJmtrawDPnwPeuHEj73liOzs7hIeHY/78+VwcIYRog+vXrwMA/P39lc5XlCvidJXayeLIkSNx9+5dLFq0CPn5+ejZsycSEhK4Riw3btyAnt5/Fyzd3d0RHR2NBQsWYP78+ejQoQPi4uLg4ODAxfz8889csgkAo0aNAgAsXrwYS5Ys4cq3bt2KNm3aYODAgZXq1aRJE6xfvx4zZ84EYwz29vZcNz+EkLrl5eUFW1tbpKam4urVqzh16hQ3gouHhweGDRsGOzs7eHl5abqqhBBSbe3btwcAxMfHY+LEiZXmx8fH8+J0lYDRw0ScoqIimJqaorCwkJ5fbEAay3HR9v1UdMrt7++PsLAwODg4IDMzExEREYiPj0dMTAwCAwM1XU21aftxqU269F6kp6fDyckJaWlpeP311zVdnVfS0I9LQ69fVcrKymBkZIQWLVrg77//hlQq5b4Iu7m5oV27drh//z5KSkogEok0XV21VffY0NjQhJBaERgYiJiYGMyaNavSbWhtTRQJIY2bSCTCzJkzsWLFChgaGkIul3Pz9PT0IJfLMWfOHK1MFNVBySIhpNYEBgZi6NChSElJ4b59e3l5QSgUarpqhBBSI66urgAq9+qgeK2Yr8u0rp9FQkjDJhQK0b9/f4wePRr9+/dvlIni+vXrYWtrC4lEAhcXF/z+++9Vxu/fvx+dO3eGRCKBo6Oj0kZ8WVlZGDJkCExNTWFkZIQ+ffrgxo0bAJ43Evz444/RqVMnGBgYoG3btpg+fToKCwt56xAIBJWmPXv21N6OE6JjFEOZDh48GI8fP8aqVaswbdo0rFq1Co8fP8bgwYMbxVCmdGWREEJq0d69exEaGoqoqCi4uLhg9erV8PX1xZUrV5R2Sp6amorRo0cjIiIC/v7+iI6ORkBAANLT07mGgNevX4enpydCQkLw+eefw8TEBBcvXoREIgEA3L59G7dv38bKlSvRtWtX/P333/jggw9w+/ZtxMTE8La3bds2DBo0iHttZmZWd28GIVpOMZTp7t27IZFI8Mknn/Dmh4WFwd3dHSkpKejfv79G6lgfKFkkhJBapOiFQdHDQ1RUFA4dOoStW7fi008/rRS/Zs0aDBo0CHPmzAEALFu2DImJiVi3bh2ioqIAAJ999hnefvttLF++nFuuYutLBwcHHDhwgDfvyy+/xNixY1FeXs7rH87MzKzGgyIQ0tgohiit2INLRYpyXR/KlG5DE0JILSkrK0NaWhp8fHy4Mj09Pfj4+EAqlSpdRiqV8uIBwNfXl4uXy+U4dOgQOnbsCF9fX1hYWMDFxQVxcXFV1kXRuvHFjoSnTp0Kc3NzODs7Y+vWrVWOrlNaWoqioiLeREhjougbNjMzEzKZDMnJydi9ezeSk5Mhk8mQmZnJi9NVdGVRB8lkMmpgQIgG3Lt3DzKZjOt3VsHS0hKXL19Wukx+fr7S+Pz8fADPR7159OgRvvrqK3zxxRf4+uuvkZCQgMDAQCQlJaFfv35K67Fs2TJMnjyZV7506VK88cYbMDQ0xNGjR/HRRx/h0aNHmD59utK6RURE4PPPP6/2/hOiaxR9yH788ce4d+8ecnNzuXm2trYwNzdvFH3IUrKoY2JjYzFr1qxKJ3RkZCR1XUKIFlJ01TF06FDMnDkTANCzZ0+kpqYiKiqqUrJYVFQEPz8/dO3alTeoAQAsXLiQ+71Xr14oKSnBihUrVCaLYWFhCA0N5a3bxsamNnaLEK0gFAoxfPhwrFixgjfgCPB8EJLc3FzMmTNH5y/I0G1oHaLoFNnR0ZE3Nq+joyOCgoIQGxur6SoSotPMzc0hFApRUFDAKy8oKFD5nKCVlVWV8ebm5tDX10fXrl15MV26dOFaQysUFxdj0KBBaNq0KX788Uc0adKkyvq6uLjgn3/+QWlpqdL5YrEYJiYmvImQxkQmk2HHjh0AUKkvRbFYDADYsWOHzreGpmRRRyia9/v7+yMuLg6urq4wNjaGq6sr4uLi4O/v3yia9xOiSSKRCE5OTjh+/DhXJpfLcfz4cbi5uSldxs3NjRcPAImJiVy8SCRCnz59cOXKFV7M1atX0a5dO+51UVERBg4cCJFIhJ9//plrKV2VjIwMNGvWjPunRwjhS05Oxp07d+Dp6Yl///2X13XOgwcP4OHhgTt37iA5OVnTVa1TdBtaR1Rs3v/ipXI9Pb1G07yfEE0LDQ3F+PHj0bt3bzg7O2P16tUoKSnhWkcHBwejdevWiIiIAADMmDED/fr1Q2RkJPz8/LBnzx6cO3cOmzZt4tY5Z84cjBw5En379oW3tzcSEhJw8OBB7h+UIlF8/Pgxdu7cyWuM0rJlSwiFQhw8eBAFBQVwdXWFRCJBYmIiwsPDMXv27Pp9gzTk8ePHvOdGs7KyeD8VOnfuDENDw3qtG2m4FH9jPj4+6NKlC+8RrzVr1mD8+PE4deoUkpOTMWDAAM1Usj4wwiksLGQAWGFhoaarorbo6GgGgBUXFyudX1RUxACw6Ojoeq7Zq9Pm46KOxrKf2qYmx2Xt2rWsbdu2TCQSMWdnZ3b69GluXr9+/dj48eN58fv27WMdO3ZkIpGIdevWjR06dKjSOrds2cLs7e2ZRCJhPXr0YHFxcdy8pKQkBkDplJOTwxhj7MiRI6xnz57M2NiYGRkZsR49erCoqCgmk8nq9L1oKNLS0lS+RxWntLQ0TVdVbQ39uDT0+lVlwYIF3LkxePBgJpVKWXFxMZNKpWzw4MHcvAULFmi6qjVS3WMjYKyKfhMaGW0e7Dw5ORne3t6QSqVKhx6SSqVwd3dHUlKS1l1Z1Objoo7Gsp/aho7Lf7T5vXjxyuKTJ0+Qm5sLW1tbGBgYcOXaeGWxoR+Xhl6/qhw9ehS+vr5o3rw5CgoKeF1RlZeXw9LSEg8ePMAvv/yCgQMHarCmNVPdY0O3oXWEonl/eHg44uLieLei5XI5IiIiGkXzfkIIUcbQ0BCvv/46r8zDw0NDtSHaQtHK+cGDB3jnnXcwf/58ODg4IDMzE+Hh4Xjw4AEvTldRsqgjhEIhIiMjERQUhICAAISFhXEndEREBOLj4xETE6PzJzQhhBBSW+7cucP9fvz4ccTHx3OvK16Brhini6g1tA4JDAxETEwMLly4AHd3d5iYmMDd3R2ZmZmIiYmhfhYJIYQQNShGZnn33XdRVlbGm1daWooxY8bw4nQVXVnUMYGBgRg6dCiN4EIIIYS8Ii8vL1hYWGDXrl14++230aFDBzx58gQGBgbIzs5GdHQ0LCwsdP4RL0oWdZBQKNS6RiyEEFJfaEhUog5FO+CkpCQcPnyYK6/YMErX0W1oQgghjUZsbCzs7e3h7e2NMWPGwNvbG/b29jTCFVEqJSUFd+/eVTpPIBAAeP68YkpKSn1Wq95RskgIIaRRoCFRibpu3boFAHjrrbdQWFiIpKQkREdHIykpCQ8fPsRbb73Fi9NVdBuaEEKIzntxSFRF92KKIVEDAgIwe/ZsDB06lG5JE47iqmJgYCCaNGlS6RGvgIAAHDlyROXVR11BVxYJIYToPMWQqPPnz1c5JGpOTo7O304k6mnZsiWA51el5XI5b55cLkdcXBwvTldRskgIIUTn5eXlAQAcHByUzleUK+IIAYDWrVsDAI4cOYKAgADe4wuKq4oV43QV3YYmhBCi8xT94GVmZiodEjUzM5MXRwjw3+ho5ubm+OOPP+Du7s7Na9euHXr37o379+/rfNc5dGWREEKIzqs4JKqy24k0JCpRRjE62rlz5yo9l3jnzh2cO3cOK1eu1PnnXClZJIQQovMU//Tj4+OV3k6Mj49vFP/0Sc0ousl5sUxZuS6iZJEQQkijQEOiEnVVbEX/4MEDrFq1CtOmTcOqVatw//59+Pv7Y/bs2ZDJZJquap2iZxYJIYQ0GjQkKlGHohX9lClT0KVLF+Tm5nLz1qxZg8mTJ+PgwYNISUnR6ZHT6MoiIYSQRkUxJOro0aPRv39/ShSJSorW8fPnz1famftnn33Gi9NVdGWREEIIIUQJCwsLAICHh4fSztz79euHkydPcnG6ipJFQgghjUpZWRk2bNiA69evo3379vjoo48gEok0XS2ihRhjmq5CvaBkkRBCSKMxd+5crFq1CuXl5VzZnDlzMHPmTCxfvlyDNSMN0Z07dwAAJ0+exNChQzFo0CAYGBjgyZMnSEhIwKlTp3hxuoqSRUIIIY3C3LlzsWLFClhaWuKLL76Av78/4uPjsWDBAqxYsQIAKGEkPIpO2t99913s3bsX8fHx3Dx9fX2MGTMG0dHROt+Zu4A1lmuo1VBUVARTU1MUFhbCxMRE09Uh/6+xHJfGsp/aho7Lf7T5vSgrK4ORkRFatGiBf/75B/r6/10rKS8vR5s2bXD//n2UlJRo3S3phn5cGnr9qiKTydCqVSvcuXMHfn5+ePvtt7kri4cPH8ahQ4dgYWGB27dva2VDqeoeG2oNTQghROdt2LAB5eXl+OKLL3iJIvD8CtHSpUtRXl6ODRs2aKiGpKFSXFMTCATo1asXgoKC0KtXr0bTITdAySIhhJBG4Pr16wAAf39/pfMV5Yo4QoDn/SzevXsXERERyMzM5HXmfvHiRYSHh+POnTtISUnRdFXrFCWLhBBCdF779u0BgPfMWUWKckUcIcB//SdOmzYNV65c4Y3gcvnyZUybNo0Xp6soWSSEEKLzPvroI+jr62PBggW8ltDA82cWFy1aBH19fXz00UcaqiFpiBQNV9atW4dOnTph5syZWLduHWbOnIlOnTph3bp1vDhdRckiIYQQnScSiTBz5kwUFBSgTZs22LRpE27fvo1NmzahTZs2KCgowMyZM7WucQupW15eXrCwsEBYWBgcHBx4I7g4ODhg/vz5sLCwgJeXl6arWqdqlCyuX78etra2kEgkcHFxwe+//15l/P79+9G5c2dIJBI4Ojri8OHDvPmxsbEYOHAgWrRoAYFAgIyMjErr6N+/PwQCAW/64IMPeDE3btyAn58fDA0NYWFhgTlz5lT6BkkIIaRxWr58OebMmYP79+9jypQpaN26NaZMmYL79+9jzpw51G0OUapipzGMMW5qTNROFvfu3YvQ0FAsXrwY6enp6NGjB3x9fVV2SJmamorRo0cjJCQE58+fR0BAAAICApCZmcnFlJSUwNPTE19//XWV2540aRLy8vK4qeIftkwmg5+fH8rKypCamoodO3Zg+/btWLRokbq7SAghREctX74cJSUlvGfPSkpKKFEkSlEDl//H1OTs7MymTp3KvZbJZKxVq1YsIiJCafyIESOYn58fr8zFxYVNmTKlUmxOTg4DwM6fP19pXr9+/diMGTNU1uvw4cNMT0+P5efnc2XfffcdMzExYaWlpS/Zq+cKCwsZAFZYWFiteFI/anJc1q1bx9q1a8fEYjFzdnZmZ86cqTJ+3759rFOnTkwsFjMHBwd26NAh3ny5XM4WLlzIrKysmEQiYQMGDGBXr16ttJ74+Hjm7OzMJBIJMzMzY0OHDq12nen8a5jouPyH3ouGqaEfl4Zev6pER0czAKy4uJiVl5ezpKQkFh0dzZKSklh5eTkrKipiAFh0dLSmq1oj1T02al1ZLCsrQ1paGnx8fLgyPT09+Pj4QCqVKl1GKpXy4gHA19dXZXxVdu3aBXNzczg4OCAsLAyPHz/mbcfR0RGWlpa87RQVFeHixYtK11daWoqioiLeRLRfXVz9Xr58Ob799ltERUXhzJkzMDIygq+vL54+fcrFHDhwAOPGjcOECRPwxx9/4NSpUxgzZkyd7y8hhKhD3UfJFPbs2QOBQICAgIC6rWADomi4UvH/QUWKcl1v4KLWlcVbt24xACw1NZVXPmfOHObs7Kx0mSZNmlTKuNevX88sLCwqxVZ1ZXHjxo0sISGB/fnnn2znzp2sdevW7J133uHmT5o0iQ0cOJC3TElJCQPADh8+rLRuixcvZgAqTdr47UeXqfuttLavfsvlcmZlZcVWrFjBzX/48CETi8Vs9+7djDHGnj17xlq3bs2+//57tfatIm3+9q3L6Lj8h96Lhkmd47Jnzx4mEonY1q1b2cWLF9mkSZOYmZkZKygoqHK5nJwc1rp1a+bl5aXWHRN169fQlJeXM1tbW9a7d29ma2vLyxUU5XZ2dqy8vFzTVa2ROrmyqEmTJ0+Gr68vHB0d8e677+J///sffvzxx1fqQDUsLAyFhYXcdPPmzVqsMdGEurj6nZOTg/z8fF6MqakpXFxcuJj09HTcunULenp66NWrF6ytrfHWW2+p/DYK0JVtQkj9++abbzBp0iRMmDABXbt2RVRUFAwNDbF161aVy8hkMrz77rv4/PPP8dprr9VjbTVPKBRi+PDhOHfuHJ48ecJrRf/kyROcO3cOQUFBWjnUnzrUShbNzc0hFApRUFDAKy8oKICVlZXSZaysrNSKry4XFxcAwLVr16rcjmKeMmKxGCYmJryJaLd79+5BJpPxHkcAAEtLS+Tn5ytdJj8/v8p4xc+qYv766y8AwJIlS7BgwQLEx8ejWbNm6N+/Px48eKB0uxERETA1NeUmGxsbNfeWEEKqryZfpgFg6dKlsLCwQEhISLW2o0tfhGUyGfbv34/evXtDLBZj8uTJaNWqFSZPngyJRILevXsjJiYGMplM01WtU2oliyKRCE5OTjh+/DhXJpfLcfz4cbi5uSldxs3NjRcPAImJiSrjq0vRvY7iOQE3NzdcuHCB91xaYmIiTExM0LVr11faFiEvI5fLAQCfffYZhg0bBicnJ2zbtg0CgQD79+9Xugxd2SaE1KeafJk+efIktmzZgs2bN1d7O7r0RTglJQW5ubkYNmwY9PT4KZNAIEBgYCBycnJ0vjW02rehQ0NDsXnzZuzYsQNZWVn48MMPUVJSggkTJgAAgoODERYWxsXPmDEDCQkJiIyMxOXLl7FkyRKcO3eOGyIHAB48eICMjAxcunQJAHDlyhVkZGRwJ+/169exbNkypKWlITc3Fz///DOCg4PRt29fdO/eHQAwcOBAdO3aFePGjcMff/yBX375BQsWLMDUqVMhFotr/g4RrVIXV78VP6uKUXxpqfjFRCwW47XXXsONGzeUbpeubBNCGrLi4mKMGzcOmzdvhrm5ebWX06Uvwoph/ObPnw9HR0dep9yOjo747LPPeHE6qyYPRK5du5a1bduWiUQi5uzszE6fPs3N69evHxs/fjwvft++faxjx45MJBKxbt26VeqWZNu2bUobmixevJgxxtiNGzdY3759WfPmzZlYLGb29vZszpw5lR7IzM3NZW+99RYzMDBg5ubmbNasWezZs2fV3i9tfghXl9Wkgcu0adO41zKZjLVu3brKBi7+/v68Mjc3t0oNXFauXMmrU8UGLorXFRu4lJWVMQsLC7Zx48Y62U9SP+i4/Ifei4apuseltLSUCYVC9uOPP/LKg4OD2ZAhQyrFnz9/ngFgQqGQmwQCARMIBEwoFLJr167Vav0aomPHjjEAzNPTk8lkMt48mUzGPD09GQB27NgxDdXw1VT32NQoWdRV2nxC6zJ1j8uePXuYWCxm27dvZ5cuXWKTJ09mZmZmXB+c48aNY59++ikXf+rUKaavr89WrlzJsrKy2OLFi1mTJk3YhQsXuJivvvqKmZmZsZ9++on9+eefbOjQoczOzo49efKEi5kxYwZr3bo1++WXX9jly5dZSEgIs7CwYA8ePKiT/ST1g47Lf+i9aJjUOS7qfJl+8uQJu3DhAm8aOnQoe+ONN9iFCxcaRR/GL0sWPTw8GkWyqF9fVzAJqS8jR47E3bt3sWjRIuTn56Nnz55ISEjgntO5ceMG79kTd3d3REdHY8GCBZg/fz46dOiAuLg4ODg4cDFz585FSUkJJk+ejIcPH8LT0xMJCQmQSCRczIoVK6Cvr49x48bhyZMncHFxwYkTJ9CsWbP623lCCKlCaGgoxo8fj969e8PZ2RmrV6+u9ChZ69atERERAYlEwvscBAAzMzMAqFSuqxTtIE6ePImAgABujOjMzExERETg1KlTvDhdRcki0UnTpk3jPRdbUXJycqWy4cOHY/jw4SrXJxAIsHTpUixdulRlTJMmTbBy5UqsXLlS7foSQkh9UPfLdGOneB49IiICGzduhLu7OzfPzs4O4eHhmD9/vs53yk3JIiGEENKIqPtluqLt27fXfoUaMC8vL9ja2iI1NRVXr17FqVOnkJeXB2tra3h4eGDYsGGws7ODl5eXpqtap+jrAyGEEEKIEkKhEJGRkYiPj8ewYcMgFovh7+8PsViMYcOGIT4+HitXrqROuQkhhKhH3bF39+/fj86dO0MikcDR0RGHDx+uFJOVlYUhQ4bA1NQURkZG6NOnD69bpqdPn2Lq1Klo0aIFjI2NMWzYsErdPd24cQN+fn4wNDSEhYUF5syZg/Ly8trZaUJ0VGBgIGJiYnDhwgW4u7vDxMQE7u7uyMzMRExMDAIDAzVdxTpHySIhhNSivXv3IjQ0FIsXL0Z6ejp69OgBX19flQ/Ap6amYvTo0QgJCcH58+cREBCAgIAA3lCR169fh6enJzp37ozk5GT8+eefWLhwIa+B1cyZM3Hw4EHs378fv/76K27fvs37JyaTyeDn54eysjKkpqZix44d2L59OxYtWlR3bwYhOiIwMBDXrl1DUlISoqOjkZSUhOzs7EaRKAKoWT+Lukqbm/frssZyXBrLfmqbmvTzOXXqVO61TCZjrVq1qrKfTz8/P16Zi4sL188nY4yNHDmSjR07VuU2Hz58yJo0acL279/PlWVlZTEATCqVMsYYO3z4MNPT0+O6kGKMse+++46ZmJg0ii5QdFlDPy4NvX6NWXWPDV1ZJITUKplMhuTkZOzevRvJyck6P2ZqRTUZe1cqlfLiAcDX15eLl8vlOHToEDp27AhfX19YWFjAxcUFcXFxXHxaWhqePXvGW0/nzp3Rtm1bbj2KEScqDvXm6+uLoqIiXLx4UWnddGmMX0Je1ZMnTzBt2jT4+vpi2rRpePLkiaarVG8oWSSE1JrY2FjY29vD29sbY8aMgbe3N+zt7REbG6vpqtWLmoy9m5+fX2X8nTt38OjRI3z11VcYNGgQjh49infeeQeBgYH49ddfuXWIRCKuDzxl61G1HcU8ZXRpjF9CXkVAQAAMDQ2xfv16HD16FOvXr4ehoSECAgI0XbV6QckiIaRWxMbGIigoSOn4qUFBQY0mYaxtcrkcADB06FDMnDkTPXv2xKeffgp/f39ERUXV6bZ1aYxfQmoqICAAP/30E0QiET799FNcu3YNn376KUQiEX766adGkTBSskgIeWUymQyzZs2Cv78/4uLi4OrqCmNjY7i6uiIuLg7+/v6YPXu2zt+SNjc3h1AorNQKuaCgAFZWVkqXsbKyqjLe3Nwc+vr66Nq1Ky+mS5cuXGtoKysrlJWV4eHDhyrXo2o7innKiMVimJiY8CZCGpMnT55wiWJxcTEiIiLQvn17REREoLi4mEsYdf2WNCWLhJBXlpKSgtzcXMyfP7/S6A96enoICwtDTk4OUlJSNFTD+iESieDk5ITjx49zZXK5HMePH4ebm5vSZdzc3HjxAJCYmMjFi0Qi9OnTB1euXOHFXL16Fe3atQMAODk5oUmTJrz1XLlyBTdu3ODW4+bmhgsXLvBaZScmJsLExKRSIkoIeW7OnDkAng+TKBKJePNEIhE++eQTXpyuohFcCCGvLC8vD4Dq8WIV5Yo4XabO2LsAMGPGDPTr1w+RkZHw8/PDnj17cO7cOWzatIlb55w5czBy5Ej07dsX3t7eSEhIwMGDB7nRNkxNTRESEoLQ0FA0b94cJiYm+Pjjj+Hm5gZXV1cAwMCBA9G1a1eMGzcOy5cvR35+PhYsWICpU6dCLBbX75tEiJbIzs4GAEycOFHp/JCQECxfvpyL01V0ZZEQ8soU46JW7BuwIkW5ro+fCjwfe3flypVYtGgRevbsiYyMjEpj71ZMmt3d3REdHY1NmzahR48eiImJQVxcHC/xfueddxAVFYXly5fD0dER33//PQ4cOABPT08uZtWqVfD398ewYcPQt29fWFlZ8Z4TFQqFiI+Ph1AohJubG8aOHYvg4OAqxzsnpLHr0KEDAOD7779XOn/Lli28OF0lYIwxTVeioSgqKoKpqSkKCwvp2ZwGpLEcF23eT5lMBnt7ezg6OiIuLo53K1oul3OdTGdnZ2vdsFjafFxqG70XDVNDPy4NvX5VefLkCQwNDSESiXD//n18//33uH79Otq3b4+JEyeiRYsWKCsrw+PHj2FgYKDp6qqtuseGbkMTQl6ZYvzUoKAgBAQEICwsDA4ODsjMzERERATi4+MRExOjdYkiIaRxMzAwwNChQ/HTTz+hadOmvHkzZ84E8LynAm1MFNVBySIhpFYoxk+dNWsW3N3duXI7O7tGM34qIUT3dOzY8ZXm6wK6DV2BNl8q12WN5bjoyn7KZDKkpKQgLy8P1tbW8PLy0uorirpyXGoDvRcNU0M/Lg29flUpKyuDgYEB5HI53nrrLRgaGuLff/9Fs2bN8PjxYxw5cgR6enp48uRJpdbS2oBuQxNCNEIoFKJ///6argYhhLyytWvXQi6Xo3v37oiPj6/0PHavXr3w559/Yu3atZg1a5YGa1q3qDU0IYQQQogSJ0+eBACEh4eDMcYb954xhmXLlvHidBVdWSSEEEIIUcLY2BgA8OOPP2Lq1Kn4+++/uXnt2rXDgAEDeHG6iq4sEkIIIYQoMW7cOADP+1NUNlzm1q1beXG6iq4sEkIIIYQoUfH5a7lcjtGjR6N37944d+4cDhw4oDROF1GySAghhBCiRMXx7MvKyrB7927s3r1baZzilrQuotvQhBBCCCFKKMZfB1BpDPWKryvG6SJKFgkhhBBClJDL5QCej2tfXl7Om1deXs6Nd6+I01V0G5oQQgghRInmzZsDAPLy8irNk8lkXLkiTldRskgIIYQQooS5uTn3e5MmTRAUFMQ1cImJicGzZ88qxekiShYJIYQQQpSQSqXc78+ePVPZwEUqlWL8+PH1WbV6Rc8sEkIIIYQoceHCBe53iUTCm2dgYKA0ThfRlUVCCCGEECUEAgH3+4ABA2Bvb48nT57AwMAA165dw6FDhyrF6SJKFgkhhBBClOjWrRtOnToFfX19HDlyhNfqWSgUQl9fH+Xl5ejWrZsGa1n36DY0IYQQQogSnp6eAJ53kyMUCvHGG29g7NixeOONN6Cnp8d1p6OI01V0ZZEQQgghRIlWrVpxvz979gwnTpx4aZwuoiuLhBBCCCFEJUoWCSGEEEKUyM/Pr9U4bUXJIiGEEEKIEgUFBbUap60oWSSEEEIIUeLOnTu1GqetKFkkhBBCCFEiLS2tVuO0VY2SxfXr18PW1hYSiQQuLi74/fffq4zfv38/OnfuDIlEAkdHRxw+fJg3PzY2FgMHDkSLFi0gEAiQkZHBm//gwQN8/PHH6NSpEwwMDNC2bVtMnz4dhYWFvDiBQFBp2rNnT012kRBCCCGNXElJCfe7WCzmzav4umKcLlI7Wdy7dy9CQ0OxePFipKeno0ePHvD19VV5CTY1NRWjR49GSEgIzp8/j4CAAAQEBCAzM5OLKSkpgaenJ77++mul67h9+zZu376NlStXIjMzE9u3b0dCQgJCQkIqxW7btg15eXncFBAQoO4uEkIIIYTg6dOn3O8vjtJS8XXFOF2kdj+L33zzDSZNmoQJEyYAAKKionDo0CFs3boVn376aaX4NWvWYNCgQZgzZw4AYNmyZUhMTMS6desQFRUFABg3bhwAIDc3V+k2HRwccODAAe51+/bt8eWXX2Ls2LEoLy+Hvv5/u2FmZgYrKyt1d4sQQgghhKfi+M9lZWW8eRVfV4zTRWpdWSwrK0NaWhp8fHz+W4GeHnx8fCCVSpUuI5VKefEA4OvrqzK+ugoLC2FiYsJLFAFg6tSpMDc3h7OzM7Zu3QrGmMp1lJaWoqioiDcRQgghhABA27Ztud8rDvX34uuKcbpIrWTx3r17kMlksLS05JVbWlqq7GMoPz9frfjq1mPZsmWYPHkyr3zp0qXYt28fEhMTMWzYMHz00UdYu3atyvVERETA1NSUm2xsbGpcJ0IIIYToltdff71W47SV1g33V1RUBD8/P3Tt2hVLlizhzVu4cCH3e69evVBSUoIVK1Zg+vTpStcVFhaG0NBQ3ropYSSEEEIIgGo/1qbrj7+pdWXR3NwcQqGwUueTBQUFKt8oKysrteKrUlxcjEGDBqFp06b48ccf0aRJkyrjXVxc8M8//6C0tFTpfLFYDBMTE95ECCGEEAI8b2Bbm3HaSq1kUSQSwcnJCcePH+fK5HI5jh8/Djc3N6XLuLm58eIBIDExUWW8KkVFRRg4cCBEIhF+/vlnSCSSly6TkZGBZs2aVWruTgghhBDyMkePHuV+b926NW9emzZtlMbpIrVvQ4eGhmL8+PHo3bs3nJ2dsXr1apSUlHCto4ODg9G6dWtEREQAAGbMmIF+/fohMjISfn5+2LNnD86dO4dNmzZx63zw4AFu3LjBZeZXrlwB8PyqpJWVFZcoPn78GDt37uQ1RmnZsiWEQiEOHjyIgoICuLq6QiKRIDExEeHh4Zg9e/arvUOEEEIIaZSys7O53x88eMCbd//+faVxukjtZHHkyJG4e/cuFi1ahPz8fPTs2RMJCQlcI5YbN25AT++/C5bu7u6Ijo7GggULMH/+fHTo0AFxcXFwcHDgYn7++Wcu2QSAUaNGAQAWL16MJUuWID09HWfOnAEA2Nvb8+qTk5MDW1tbNGnSBOvXr8fMmTPBGIO9vT3XzQ8hhBBCiLqq+3iarj/GJmBV9S3TyBQVFcHU1JTrloc0DI3luDSW/dQ2dFz+Q+9Fw9TQj0tDr19VvvrqK4SFhQF43s6hYhuIiq8jIiKU9jXd0FX32NDY0IQQQgghSjg5OXG/v9hYtuLrinG6iJJFQgghhBAlVA1lXNM4bUXJIiGEEEKIEnfv3gUA9OjRQ+l8RbkiTldpXafc5OVkMhlSUlKQl5cHa2treHl5QSgUarpahBBCiFZp2bIlAOCPP/6ARCLB06dPuXkSiQR//PEHL05X0ZVFHRMbGwt7e3t4e3tjzJgx8Pb2hr29PWJjYzVdNUIIIUSrVBxApKysjDev4msawYVojdjYWAQFBcHR0RFSqRTFxcWQSqVwdHREUFAQJYyEEEKIGmQyGff7iwN8VHxdMU4XUbKoI2QyGWbNmgV/f3/ExcXB1dUVxsbGcHV1RVxcHPz9/TF79mydP6EJaQjWr18PW1tbSCQSuLi44Pfff68yfv/+/ejcuTMkEgkcHR1x+PBh3vz33nsPAoGANw0aNIibn5ycXGm+Yjp79iwAIDc3V+n806dP1/4bQIiO+PXXX7nf33jjDaxbtw5btmzBunXr8MYbbyiN00X0zKKOSElJQW5uLnbv3s3rFB0A9PT0EBYWBnd3d6SkpKB///6aqSQhjcDevXsRGhqKqKgouLi4YPXq1fD19cWVK1dgYWFRKT41NRWjR49GREQE/P39ER0djYCAAKSnp/MGLxg0aBC2bdvGva54VcPd3R15eXm89S5cuBDHjx9H7969eeXHjh1Dt27duNctWrR45X0mRFfdvHkTADBx4kQcO3YMhw4d4ubZ2dnh/fffx9atW7k4XUXJoo5Q/KOo+M+lIkX5i/9QCCG1SzFylGJUqqioKBw6dAhbt25V2mnvmjVrMGjQIMyZMwcAsGzZMiQmJmLdunWIiori4sRiscrnokQiEW/es2fP8NNPP+Hjjz+GQCDgxbZo0ULnn68ipLbY2NgAADIzM3H16lWcOnWKazzq4eGBvn378uJ0Fd2G1hHW1tYAnp/QyijKFXGEkNpXVlaGtLQ0+Pj4cGV6enrw8fGBVCpVuoxUKuXFA4Cvr2+l+OTkZFhYWKBTp0748MMPeePSvujnn3/G/fv3ecOoKgwZMgQWFhbw9PTEzz//XOX+lJaWoqioiDcR0pgobjWfPn0aAwYMwJUrV1BSUoIrV65gwIAB3GMc7u7umqxmnaMrizrCy8sLtra2CA8PR1xcHO9WtFwuR0REBOzs7ODl5aXBWhKi2+7duweZTAZLS0teuaWlJS5fvqx0mfz8fKXx+fn53OtBgwYhMDAQdnZ2uH79OubPn4+33noLUqlUabdYW7Zsga+vL9q0acOVGRsbIzIyEh4eHtDT08OBAwcQEBCAuLg4DBkyRGndIiIi8Pnnn1d7/7UFdS9Gqqt///5o1qwZ/v33X6SkpCAlJUVpnLJHTHQKI5zCwkIGgBUWFmq6KjVy4MABJhAI2ODBg1lqaiorKipiqampbPDgwUwgELADBw5ouoo1UpPjsm7dOtauXTsmFouZs7MzO3PmTJXx+/btY506dWJisZg5ODiwQ4cO8ebL5XK2cOFCZmVlxSQSCRswYAC7evUqL6Zdu3YMAG+KiIio0/0kdU+d43Lr1i0GgKWmpvLK58yZw5ydnZUu06RJExYdHc0rW79+PbOwsFC5nevXrzMA7NixY5Xm3bx5k+np6bGYmJiX1nfcuHHM09NT5fynT5+ywsJCbrp586bWn6MHDhxgtra2vL9TW1tbrf18ZKzhf3Y09Pq9THR0NAPAxGIx77yRSCQMAFuxYgUrKSnRdDVrpLrHhm5D65DAwEDExMTgwoULcHd3h4mJCdzd3ZGZmYmYmBgEBgZquor1QtHAYPHixUhPT0ePHj3g6+urcjgmRQODkJAQnD9/HgEBAQgICODd0l++fDm+/fZbREVF4cyZMzAyMoKvry+vg1YAWLp0KfLy8rjp448/rtN9JQ2Lubk5hEIhCgoKeOUFBQUqnxO0srJSKx4AXnvtNZibm+PatWuV5m3btg0tWrRQebWwIhcXF6XrUBCLxTAxMeFN2oy6FyM1MXr0aBw4cKDS36SVlRUOHDiA2bNnw9DQUEO1qyf1lLxqBW3/9qNQXl7OkpKSWHR0NEtKSmLl5eWartIrUfe4ODs7s6lTp3KvZTIZa9WqlcqrfCNGjGB+fn68MhcXFzZlyhTG2POrilZWVmzFihXc/IcPHzKxWMx2797NlbVr146tWrWqurtVia6cf7qmJufftGnTuNcymYy1bt26yvPP39+fV+bm5sadf8rcvHmTCQQC9tNPP/HK5XI5s7OzY7NmzapWXSdOnMh69epVrVjGtPscLS8vZ7a2tmzw4MFMJpPx5slkMjZ48GBmZ2enlZ+XDf24NPT6VVd5eTnbuHEjA8A2btyolefKi6p7bChZrEBXTmhdo85xKS0tZUKhkP3444+88uDgYDZkyBCly9jY2FRK8hYtWsS6d+/OGPvvlt/58+d5MX379mXTp0/nXrdr145ZWlqy5s2bs549e7Lly5ezZ8+eqayrLt7i00Xqfi7s2bOHicVitn37dnbp0iU2efJkZmZmxvLz8xljz2/9fvrpp1z8qVOnmL6+Plu5ciXLyspiixcvZk2aNGEXLlxgjDFWXFzMZs+ezaRSKcvJyWHHjh1jr7/+OuvQoQN7+vQpb9vHjh1jAFhWVlalem3fvp1FR0ezrKwslpWVxb788kump6fHtm7dWmfvRUOSlJTEADCpVKp0fmpqKgPAkpKS6rditaChH5eGXj91pKWlMQAsLS1N01WpFXQbmjRKVTUwqNhgoKKXNTBQ/HzZOqdPn449e/YgKSkJU6ZMQXh4OObOnauyrhERETA1NeUmXe96obEYOXIkVq5ciUWLFqFnz57IyMhAQkICd/7cuHGD14WVu7s7oqOjsWnTJvTo0QMxMTGIi4vjursSCoX4888/MWTIEHTs2BEhISFwcnJCSkpKpREltmzZAnd3d3Tu3Flp3ZYtWwYnJye4uLjgp59+wt69e5W2mNZF1L3Yf9TpNH7z5s3w8vJCs2bN0KxZM/j4+Ly0k3mie6g1NCG1JDQ0lPu9e/fuEIlEmDJlCiIiIir9UweAsLAw3jJFRUWUMOqIadOmYdq0aUrnJScnVyobPnw4hg8f/n/t3X1QFPf9B/A3nN4dKmKUyonV3LWiaEQJUPCs1joyosOoNz5ETQnE8alWE/U62sIgEE16+EBiHmz8mcTINBKQiaXWOFhDpDrx1PEQrQafWihm9PChhUNUEPj+/jCcrtwajoJ33L1fMzvKfj+7+93bz+DHvf1+12G8n58fDh482Kbj5uTkyLYlJSUhKSmpTfvxRI9PLzZmzJhW7d4yvZizk8YXFxdj/vz5GDt2LNRqNTZu3IjJkyfj/PnzGDhwoAvOgFyBdxbJo3TGAIOWP50dhBATE4PGxkZUVFQ4bPe0wQNE7uzx6cWam5slbd40vdjjk8aPGDEC27dvR48ePbBz506H8bt378ZvfvMbhIeHIzQ0FB9//DGam5tRVFT0jHtOrsRikTyKUqlEZGSk5BdZyy82vV7vcBu9Xt/qF9+hQ4fs8TqdDhqNRhJjs9lw4sQJ2X0CQGlpKXx9fT1//i2iLkChUCArKwv79++HwWCQjIY2GAzYv38/tmzZ4tHzLbZn0vgn3b17Fw8ePEDfvn1lYziZu+fh19DkcYxGI5KSkhAVFYXo6Ghs3boVdXV19mezEhMTMXDgQJhMJgDAypUrMWHCBGRlZSE+Ph65ubk4deoUduzYAQDw8fHBqlWr8OabbyIkJAQ6nQ7r1q1DcHAwDAYDgIdv4Thx4gQmTpwIf39/mM1mrF69GgkJCXjuuedc8jkQkVTL9GK//e1vJW/c0Ol0XjG9WHsmjX/S7373OwQHB7d669DjPHUyd2/GYpE8zty5c3Hz5k2kpaXBarUiPDy81QCDx99w0zLAIDU1FSkpKQgJCZEMMACAtWvXoq6uDkuWLEF1dTXGjRuHwsJCqNVqAA+/Us7NzUVGRgbq6+uh0+mwevVqyTOJROR6M2fOxIwZM/gGl3bIzMxEbm4uiouL7b/7HOHz2J6HxSJ5pI4cYAA8vLu4fv16rF+/3mF7RESE/R2hROTeFAoFfvnLX7q6G89ce57pbrFlyxZkZmbiq6++wqhRo54aq1KpHA7qo66LzywSERF5gfY80w08fIPVhg0bUFhYiKioqGfRVXIzvLNIRETkJZx9pnvjxo1IS0tDTk4OtFqtfW7ZXr16oVevXi47D3q2WCwSERF5CWef6f7www/R0NCA2bNnS/aTnp6OjIyMZ9l1ciEWi0RERF7EmWe65eaJJe/CZxaJiIiISBaLRSIiIiKSxWKRiIiIiGSxWCQiIiIiWSwWiYiIiEgWi0UiIiIiksVikYiIiIhksVgkIiIiIlksFomIiIhIFotFIiIiIpLFYpGIiIiIZLFYJCIiIiJZLBaJiIiISFa7isVt27ZBq9VCrVYjJiYGJ0+efGp8fn4+QkNDoVarERYWhgMHDkja9+7di8mTJ6Nfv37w8fFBaWlpq33cv38fy5cvR79+/dCrVy/MmjULVVVVkpjKykrEx8ejR48e6N+/P9asWYPGxsb2nCIRERERoR3FYl5eHoxGI9LT01FSUoLRo0cjLi4ON27ccBh/7NgxzJ8/HwsXLsTp06dhMBhgMBhw7tw5e0xdXR3GjRuHjRs3yh539erV+Otf/4r8/Hz8/e9/x7Vr1zBz5kx7e1NTE+Lj49HQ0IBjx44hOzsbu3btQlpamrOnSEREREQthJOio6PF8uXL7T83NTWJ4OBgYTKZHMa/9NJLIj4+XrIuJiZGLF26tFVseXm5ACBOnz4tWV9dXS26d+8u8vPz7evKysoEAGE2m4UQQhw4cED4+voKq9Vqj/nwww9F7969RX19fZvOraamRgAQNTU1bYqnZ8Nbrou3nGdXw+vyCD8L9+Tu18Xd++cMi8UiAAiLxeLqrnSItl4bp+4sNjQ0wGKxIDY21r7O19cXsbGxMJvNDrcxm82SeACIi4uTjXfEYrHgwYMHkv2EhoZi8ODB9v2YzWaEhYUhKChIchybzYbz58873G99fT1sNptkISIiIqJHnCoWb926haamJklBBgBBQUGwWq0Ot7FarU7Fy+1DqVSiT58+svuRO05LmyMmkwkBAQH2ZdCgQW3uExEREZE38OrR0MnJyaipqbEvV69edXWXiIiIiNxKN2eCAwMDoVAoWo1CrqqqgkajcbiNRqNxKl5uHw0NDaiurpbcXXx8PxqNptWo7Jbjyh1LpVJBpVK1uR9ERERE3sapYlGpVCIyMhJFRUUwGAwAgObmZhQVFWHFihUOt9Hr9SgqKsKqVavs6w4dOgS9Xt/m40ZGRqJ79+4oKirCrFmzAAAXL15EZWWlfT96vR5vvfUWbty4gf79+9uP07t3b4wYMcKZ0yQiIiIvdvnyZdTW1rZaX1ZWJvnTEX9/f4SEhHRa31zBqWIRAIxGI5KSkhAVFYXo6Ghs3boVdXV1WLBgAQAgMTERAwcOhMlkAgCsXLkSEyZMQFZWFuLj45Gbm4tTp05hx44d9n3+5z//QWVlJa5duwbgYSEIPLwjqNFoEBAQgIULF8JoNKJv377o3bs3XnvtNej1eowZMwYAMHnyZIwYMQKvvPIKNm3aBKvVitTUVCxfvpx3D4mIiKhNLl++jKFDhz41JiEh4antly5d8qiC0elice7cubh58ybS0tJgtVoRHh6OwsJC+2CSyspK+Po+ehRy7NixyMnJQWpqKlJSUhASEoKCggKMHDnSHrNv3z57sQkA8+bNAwCkp6cjIyMDAPDOO+/A19cXs2bNQn19PeLi4vDHP/7Rvo1CocD+/fuxbNky6PV69OzZE0lJSVi/fr2zp0hERERequWO4meffYbhw4dL2u7du4eKigpotVr4+fm12rasrAwJCQkO70p2ZT5CCOHqTrgLm82GgIAA1NTUoHfv3q7uDn3PW66Lt5xnV8Pr8gg/C/fk7tfF3fv3pJKSEkRGRsJisSAiIuKZbesKbb02Xj0amoiIiIiejsUiEREREclisUhEREREslgsEhEREZEsFotEREREJIvFIhERERHJYrFIRERERLJYLBIRERGRLBaLREQdbNu2bdBqtVCr1YiJicHJkyefGp+fn4/Q0FCo1WqEhYXhwIEDkvZXX30VPj4+kmXKlCmSGK1W2yomMzNTEnP27FmMHz8earUagwYNwqZNmzrmhInIo7FYJCLqQHl5eTAajUhPT0dJSQlGjx6NuLg43Lhxw2H8sWPHMH/+fCxcuBCnT5+GwWCAwWDAuXPnJHFTpkzB9evX7cvnn3/eal/r16+XxLz22mv2NpvNhsmTJ+P555+HxWLB5s2bkZGRgR07dnTsB0BEHofFIhFRB3r77bexePFiLFiwACNGjMD27dvRo0cP7Ny502H8u+++iylTpmDNmjUYPnw4NmzYgIiICHzwwQeSOJVKBY1GY1+ee+65Vvvy9/eXxPTs2dPetnv3bjQ0NGDnzp144YUXMG/ePLz++ut4++23O/YDICKPw2KRiKiDNDQ0wGKxIDY21r7O19cXsbGxMJvNDrcxm82SeACIi4trFV9cXIz+/ftj2LBhWLZsGW7fvt1qX5mZmejXrx9efPFFbN68GY2NjZLj/OIXv4BSqZQc5+LFi/jvf//rsG/19fWw2WyShYi8TzdXd4CIyFPcunULTU1NCAoKkqwPCgrChQsXHG5jtVodxlutVvvPU6ZMwcyZM6HT6fDPf/4TKSkpmDp1KsxmMxQKBQDg9ddfR0REBPr27Ytjx44hOTkZ169ft985tFqt0Ol0rY7T0uboTqXJZMIbb7zh5KdA1PVpevnAr/oScM25e2p+1Zeg6eXTSb1yHRaLRERubt68efa/h4WFYdSoUfjpT3+K4uJiTJo0CQBgNBrtMaNGjYJSqcTSpUthMpmgUqnaddzk5GTJfm02GwYNGtTOsyDqOpZGKjH8yFLgiHPbDf9+W0/DYpGIqIMEBgZCoVCgqqpKsr6qqgoajcbhNhqNxql4APjJT36CwMBAXLlyxV4sPikmJgaNjY2oqKjAsGHDZI/T0gdHVCpVuwtNoq7s/ywNmJu2C8NDQ53aruzCBfxf1suY3kn9chU+s0hE1EGUSiUiIyNRVFRkX9fc3IyioiLo9XqH2+j1ekk8ABw6dEg2HgC+++473L59GwMGDJCNKS0tha+vL/r3728/zpEjR/DgwQPJcYYNG+bwK2gib2a9I3Cvz1AgONyp5V6fobDeES7qdedhsUhE1IGMRiM++ugjZGdno6ysDMuWLUNdXR0WLFgAAEhMTERycrI9fuXKlSgsLERWVhYuXLiAjIwMnDp1CitWrAAA3LlzB2vWrMHx48dRUVGBoqIizJgxA0OGDEFcXByAh4NXtm7dijNnzuBf//oXdu/ejdWrVyMhIcFeCL788stQKpVYuHAhzp8/j7y8PLz77ruSr5mJiBzh19BERB1o7ty5uHnzJtLS0mC1WhEeHo7CwkL7YJLKykr4+j76f/rYsWORk5OD1NRUpKSkICQkBAUFBRg5ciQAQKFQ4OzZs8jOzkZ1dTWCg4MxefJkbNiwwf4VsUqlQm5uLjIyMlBfXw+dTofVq1dLCsGAgAD87W9/w/LlyxEZGYnAwECkpaVhyZIlz/DTIaKuiMUiEVEHW7Fihf3O4JOKi4tbrZszZw7mzJnjMN7Pzw8HDx586vEiIiJw/PjxH+zXqFGjcPTo0R+MIyJ6HL+GJiIiIiJZLBaJiIiISBa/hiYiIiL63t27dwEAJSUlrdru3buHiooKaLVa+Pn5tWovKyvr9P65AotFIiIiou+1vG1p8eLF7d6Hv79/R3XHLbBYJCIiIvqewWAAAISGhqJHjx6StrKyMiQkJOCzzz7D8OHDHW7v7++PkJCQzu7mM8VikYiIiOh7gYGBWLRo0VNjhg8fjoiIiGfUI9fjABciIiIiksVikYiIiIhksVgkIiIiIlksFomIiIhIFotFIiIiIpLF0dAeqKmpCUePHsX169cxYMAAjB8/HgqFwtXdIi/B/CN3xxwlcg7vLHqYvXv3YsiQIZg4cSJefvllTJw4EUOGDMHevXtd3bVnatu2bdBqtVCr1YiJicHJkyefGp+fn4/Q0FCo1WqEhYXhwIEDknYhBNLS0jBgwAD4+fkhNjYWly9fdriv+vp6hIeHw8fHB6WlpR11Sl0C84/cHXOUyHksFj3I3r17MXv2bISFhcFsNqO2thZmsxlhYWGYPXu21/wyzMvLg9FoRHp6OkpKSjB69GjExcXhxo0bDuOPHTuG+fPnY+HChTh9+jQMBgMMBgPOnTtnj9m0aRPee+89bN++HSdOnEDPnj0RFxeH+/fvt9rf2rVrERwc3Gnn566Yf+TumKNE7STIrqamRgAQNTU1ru6K0xobG4VWqxXTpk0TTU1NkrampiYxbdo0odPpRGNjo4t62H7OXpfo6GixfPly+89NTU0iODhYmEwmh/EvvfSSiI+Pl6yLiYkRS5cuFUII0dzcLDQajdi8ebO9vbq6WqhUKvH5559Ltjtw4IAIDQ0V58+fFwDE6dOn29RnIZh/7qorX5eO1pU/C+ao67h7/5xhsVgEAGGxWFzdlQ7R1mvDO4se4ujRo6ioqEBKSgp8faWX1dfXF8nJySgvL8fRo0dd1MNno6GhARaLBbGxsfZ1vr6+iI2NhdlsdriN2WyWxANAXFycPb68vBxWq1USExAQgJiYGMk+q6qqsHjxYvzpT39q9YooR+rr62Gz2SRLV8X8I3fHHKX2unv3LkpKSlBSUoKysjIAD1/717KupKQEd+/edXEvOxcHuHiI69evAwBGjhzpsL1lfUucp7p16xaampoQFBQkWR8UFGR/OfyTrFarw3ir1Wpvb1knFyOEwKuvvopf//rXiIqKQkVFxQ/21WQy4Y033mjTebk75h+5O+YotdeFCxcQGRkpWZeQkCD52WKxePTr/1gseogBAwYAAM6dO4cxY8a0am95/q4ljjrW+++/j9raWiQnJ7d5m+TkZBiNRvvPNpsNgwYN6ozudTrmH7k75ii1V2hoKCwWCwDg3r17qKiogFarhZ+fnyTGk7FY9BDjx4+HVqvFH/7wBxQUFEi+ZmlubobJZIJOp8P48eNd2MvOFxgYCIVCgaqqKsn6qqoqaDQah9toNJqnxrf8WVVVJfmHpKqqCuHh4QCAr7/+GmazGSqVSrKfqKgo/OpXv0J2dnar46pUqlbxXRXzj9wdc5Taq0ePHpK7hj//+c9d2BvX4DOLHkKhUCArKwv79++HwWCQjPQzGAzYv38/tmzZ4vFziSmVSkRGRqKoqMi+rrm5GUVFRdDr9Q630ev1kngAOHTokD1ep9NBo9FIYmw2G06cOGGPee+993DmzBmUlpaitLTUPvVOXl4e3nrrrQ49R3fE/CN3xxx9pKOnFiMv0J7RMx988IF4/vnnhUqlEtHR0eLEiRNPjd+zZ48YNmyYUKlUYuTIkeLLL7+UtDc3N4t169YJjUYj1Gq1mDRpkrh06ZK9/fDhwwKAw+XkyZNCCCHKy8sdtpvN5jaflyeM2Priiy+EVquVfAY6nU588cUXru5auzl7XXJzc4VKpRK7du0S3377rViyZIno06ePsFqtQgghXnnlFfH73//eHv/NN9+Ibt26iS1btoiysjKRnp4uunfvLv7xj3/YYzIzM0WfPn3EX/7yF3H27FkxY8YModPpxL179xz2oSUfvWU0dAvmn2fzhM/C23M0NzdXKJVKsXPnTnH+/HmxePFi0adPH1FVVeUw/ptvvhEKhUJs2rRJfPvttyI1NbXV78eO7B89W229Nk4Xi52RaJmZmSIgIEAUFBSIM2fOiOnTp0v+Ia6vrxfXr1+XLIsWLRI6nU40NzcLIR794/zVV19J4hoaGtp8bp6S0I2NjeLw4cMiJydHHD58uEtOBfG49lyX999/XwwePFgolUoRHR0tjh8/bm+bMGGCSEpKksTv2bNHDB06VCiVSvHCCy/I/ocmKChIqFQqMWnSJHHx4kXZ43trsSgE88+Tecpn4c052tFTi3V0/+jZ6rRi0ZVz2LVoaGgQP/rRj8T69evt69rzj/OTmNDuyVuui7ecZ1fD6/IIPwv31NbrUl9fLxQKhfjzn/8sWZ+YmCimT5/ucJtBgwaJd955R7IuLS1NjBo1SvY49+/fFzU1Nfbl6tWrzBs31SnzLLpyDrvH7du3D7dv38aCBQtatU2fPh39+/fHuHHjsG/fvqeejyfNc0dERPQ0T5tarGUasCf90NRijphMJgQEBNiXrjrLAz3iVLHYGYnWljnsnvTJJ58gLi4OP/7xj+3revXqhaysLOTn5+PLL7/EuHHjYDAYnlowMqGJiIg6VnJyMmpqauzL1atXXd0l+h91ualzvvvuOxw8eBB79uyRrA8MDJTMWfezn/0M165dw+bNmzF9+nSH+/Kkee6IiIiepjOmFnPEk6YFo4ecurPY2XPYtWWfn376Kfr16ydbAD4uJiYGV65ckW1XqVTo3bu3ZCEiIvJEnTG1GHkHp4pFV81h10IIgU8//RSJiYno3r37D/a3tLSUs/ETERF9z2g04qOPPkJ2djbKysqwbNky1NXV2ccAJCYmSt5EtXLlShQWFiIrKwsXLlxARkYGTp06hRUrVrjqFMgFnP4a2mg0IikpCVFRUYiOjsbWrVtbJdrAgQNhMpkAPEy0CRMmICsrC/Hx8cjNzcWpU6ewY8cOAICPjw9WrVqFN998EyEhIdDpdFi3bh2Cg4NhMBgkx/76669RXl6ORYsWtepXdnY2lEolXnzxRQDA3r17sXPnTnz88cfOniIREZFHmjt3Lm7evIm0tDRYrVaEh4ejsLDQPm6gsrJS8nabsWPHIicnB6mpqUhJSUFISAgKCgpk37FNnsnpYrEzEm3t2rWoq6vDkiVLUF1djXHjxqGwsBBqtVpy7E8++QRjx46VfQfjhg0b8O9//xvdunVDaGgo8vLyMHv2bGdPkYiIyGOtWLFC9s5gcXFxq3Vz5szBnDlzOrlX5M58hBDC1Z1wFzabDQEBAaipqeHzi27EW66Lt5xnV8Pr8gg/C/fk7tfF3fvnzdp6bfhuaCIiIiKSxWKRiIiIiGR1uXkWO1PLN/J8k4t7abkenv7EBPPPPXlL/rUFc9Q9uXuOMm/cV1tzh8XiY2prawGAE3O7qdraWgQEBLi6G52G+efePD3/2oI56t7cNUeZN+7vh3KHA1we09zcjGvXrsHf3x8+Pj6u7s7/pOVtNFevXu3yDxQLIVBbW4vg4GDJSHtPw/xzT96Sf23BHHVP7p6jzBv31dbcYbHooTj6jFyJ+UfujjlK7eGteeN+/wUhIiIiIrfBYpGIiIiIZLFY9FAqlQrp6elQqVSu7gp5IeYfuTvmKLWHt+YNn1kkIiIiIlm8s0hEREREslgsEhEREZEsFotEREREJIvFIhERERHJYrFIRERERLJYLHqYI0eOYNq0aQgODoaPjw8KCgpc3SXyIsw/cnfMUWovb84dFosepq6uDqNHj8a2bdtc3RXyQsw/cnfMUWovb86dbq7uAHWsqVOnYurUqa7uBnkp5h+5O+YotZc35w7vLBIRERGRLBaLRERERCSLxSIRERERyWKxSERERESyWCwSERERkSyOhvYwd+7cwZUrV+w/l5eXo7S0FH379sXgwYNd2DPyBsw/cnfMUWovb84dHyGEcHUnqOMUFxdj4sSJrdYnJSVh165dz75D5FWYf+TumKPUXt6cOywWiYiIiEgWn1kkIiIiIlksFomIiIhIFotFIiIiIpLFYpGIiIiIZLFYJCIiIiJZLBaJiIiISBaLRSIiIiKSxWKRiIiIiGSxWCQiIiIiWSwWiYiIiEgWi0UiIiIikvX/PMdOkHu5c7kAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -472,11 +486,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!rm -rf project config.yaml eval.log"
+    "!rm -rf project config_full.yaml eval.log\n"
    ]
   },
   {
@@ -489,7 +503,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "apax311",
+   "display_name": "new_defaults",
    "language": "python",
    "name": "python3"
   },
@@ -503,7 +517,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/02_Molecular_Dynamics.ipynb b/examples/02_Molecular_Dynamics.ipynb
index 83b414e8..35d5f1a2 100644
--- a/examples/02_Molecular_Dynamics.ipynb
+++ b/examples/02_Molecular_Dynamics.ipynb
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!apax template train    # generating the config file in the cwd"
+    "!apax template train --full  # generating the config file in the cwd"
    ]
   },
   {
@@ -50,7 +50,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epochs: 100%|█████████████████████████████████████| 100/100 [00:47<00:00,  2.09it/s, val_loss=0.105]\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732124364.370900  457478 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732124364.374063  457478 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:39:43 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:49<00:00,  2.01it/s, val_loss=0.0176]\n"
      ]
     }
    ],
@@ -74,14 +78,14 @@
     "\n",
     "\n",
     "# Modify the config file (can be done manually)\n",
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 100,\n",
     "    \"data\": {\n",
     "        \"n_train\": 990,\n",
     "        \"n_valid\": 10,\n",
-    "        \"valid_batch_size\": 1,\n",
+    "        \"valid_batch_size\": 10,\n",
     "        \"experiment\": experiment,\n",
     "        \"directory\": \"project/models\",\n",
     "        \"data_path\": str(train_file_path),\n",
@@ -89,10 +93,9 @@
     "        \"energy_unit\": \"kcal/mol\",\n",
     "        \"pos_unit\": \"Ang\",\n",
     "    },\n",
-    "    \"model\": {\"descriptor_dtype\": \"fp64\"},\n",
     "}\n",
     "config_dict = mod_config(config_path, config_updates)\n",
-    "with open(\"config.yaml\", \"w\") as conf:\n",
+    "with open(\"config_full.yaml\", \"w\") as conf:\n",
     "    yaml.dump(config_dict, conf, default_flow_style=False)\n",
     "\n",
     "# Train model\n",
@@ -139,7 +142,7 @@
     "    atoms=atoms,\n",
     "    timestep=0.5 * units.fs,\n",
     "    temperature_K=300,\n",
-    "    friction=0.01 / units.fs,\n",
+    "    friction=0.001 / units.fs,\n",
     ")\n",
     "\n",
     "traj = Trajectory(\"example.traj\", \"w\", atoms)\n",
@@ -182,7 +185,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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QIBAIPaqqqsIZNgAAaGPCCpQRI0Zo165d2rlzZ+gxdOhQFRYWhv53p06dVF5eHnrNvn37dOjQIfl8PkmSz+fTrl27VFNTE9qnrKxMHo9Hubm5jX7d5ORkeTyeBg8AANB+hfUelG7duql///4NtnXp0kUZGRmh7ePGjdPUqVPVvXt3eTwePfjgg/L5fLr++uslSbfccotyc3M1ZswYzZs3T36/X48//riKioqUnJwcoWkBAIC2LOw3yV7I888/r/j4eI0aNUq1tbXKz8/Xyy+/HHo+ISFBa9eu1cSJE+Xz+dSlSxeNHTtWs2fPjvRQAABAGxXnnHOxHkS4gsGg0tLSFAgE+HUP1LtkXayHAJjz9dyCWA8BOE84P7/5WzwAAMAcAgUAAJhDoAAAAHMi/iZZoKl47wgA4L/hCgoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADAnMdYDAABEXu+SdRE5ztdzCyJyHCBcXEEBAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJiTGOsBAADs6l2yLiLH+XpuQUSOg46DKygAAMAcAgUAAJhDoAAAAHN4DwrCFqnfSQMA8N9wBQUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwJ6xAKS0t1bXXXqtu3bopMzNTI0eO1L59+xrsc/LkSRUVFSkjI0Ndu3bVqFGjVF1d3WCfQ4cOqaCgQJ07d1ZmZqamTZumM2fOtHw2AACgXQgrUDZu3KiioiJt3rxZZWVlOn36tG655RadOHEitM+UKVP03nvvafXq1dq4caMOHz6su+66K/R8XV2dCgoKdOrUKX3yySdavny5li1bplmzZkVuVgAAoE2Lc8655r74hx9+UGZmpjZu3Kjf/va3CgQCuuSSS7Ry5Ur94Q9/kCR98cUX6tevnyoqKnT99dfr/fff1x133KHDhw/L6/VKkpYsWaJHH31UP/zwg5KSki74dYPBoNLS0hQIBOTxeJo7fDQTN2oDEC7+WCCk8H5+t+g9KIFAQJLUvXt3SVJlZaVOnz6tvLy80D59+/ZVr169VFFRIUmqqKjQgAEDQnEiSfn5+QoGg9q9e3ejX6e2tlbBYLDBAwAAtF/NDpT6+npNnjxZw4cPV//+/SVJfr9fSUlJSk9Pb7Cv1+uV3+8P7fPLODn7/NnnGlNaWqq0tLTQo2fPns0dNgAAaAOaHShFRUX6/PPPtWrVqkiOp1EzZsxQIBAIPaqqqqL+NQEAQOw0648FFhcXa+3atdq0aZMuu+yy0PasrCydOnVKR48ebXAVpbq6WllZWaF9tm7d2uB4Zz/lc3afcyUnJys5Obk5QwUAAG1QWFdQnHMqLi7WmjVrtGHDBuXk5DR4fsiQIerUqZPKy8tD2/bt26dDhw7J5/NJknw+n3bt2qWamprQPmVlZfJ4PMrNzW3JXAAAQDsR1hWUoqIirVy5Un/729/UrVu30HtG0tLSlJqaqrS0NI0bN05Tp05V9+7d5fF49OCDD8rn8+n666+XJN1yyy3Kzc3VmDFjNG/ePPn9fj3++OMqKiriKgkAAJAUZqAsXrxYkvS73/2uwfalS5fqvvvukyQ9//zzio+P16hRo1RbW6v8/Hy9/PLLoX0TEhK0du1aTZw4UT6fT126dNHYsWM1e/bsls0EAAC0Gy26D0qscB+U2OI+KADCxX1QILXifVAAAACigUABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMCcsP5YIAAAzRGpv+HF3/TpOLiCAgAAzCFQAACAOQQKAAAwh0ABAADm8CbZDiRSb1IDACDauIICAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcxJjPQBcWO+SdbEeAgAArYorKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCH+6AAANqMSN0X6uu5BRE5DqKHKygAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmJMZ6AAAAtLbeJesicpyv5xZE5Dg4H4ESRZH6DwAAgI6GX/EAAABzCBQAAGAOgQIAAMzhPSgAADQTb7aNHq6gAAAAcwgUAABgDr/iaQQfDwYAILa4ggIAAMwhUAAAgDkxDZRFixapd+/eSklJ0bBhw7R169ZYDgcAABgRs0D561//qqlTp+qJJ57Q9u3bdc011yg/P181NTWxGhIAADAiZm+SnT9/vsaPH6/7779fkrRkyRKtW7dOr7/+ukpKSmI1LAAA2rT2cm+WmATKqVOnVFlZqRkzZoS2xcfHKy8vTxUVFeftX1tbq9ra2tC/A4GAJCkYDEZlfPW1P0fluAAANKbXlNWxHsJ5ovEz9uwxnXMX3DcmgfLjjz+qrq5OXq+3wXav16svvvjivP1LS0v15JNPnre9Z8+eURsjAAAdWdoL0Tv2sWPHlJaW9qv7tIn7oMyYMUNTp04N/bu+vl5HjhxRRkaG4uLiovq1g8GgevbsqaqqKnk8nqh+LYs6+vwl1kBiDTr6/CXWQGINpJavgXNOx44dU3Z29gX3jUmgXHzxxUpISFB1dXWD7dXV1crKyjpv/+TkZCUnJzfYlp6eHs0hnsfj8XTYb0iJ+UusgcQadPT5S6yBxBpILVuDC105OSsmn+JJSkrSkCFDVF5eHtpWX1+v8vJy+Xy+WAwJAAAYErNf8UydOlVjx47V0KFDdd111+mFF17QiRMnQp/qAQAAHVfMAuWee+7RDz/8oFmzZsnv92vgwIFav379eW+cjbXk5GQ98cQT5/2KqaPo6POXWAOJNejo85dYA4k1kFp3DeJcUz7rAwAA0Ir4WzwAAMAcAgUAAJhDoAAAAHMIFAAAYE6HC5RFixapd+/eSklJ0bBhw7R169b/uu/p06c1e/ZsXXnllUpJSdE111yj9evXt+iYFkR6DUpLS3XttdeqW7duyszM1MiRI7Vv375oT6PZovE9cNbcuXMVFxenyZMnR2HkkRONNfjuu+907733KiMjQ6mpqRowYIA+/fTTaE6jRSK9BnV1dZo5c6ZycnKUmpqqK6+8Uk899VST/uZIa9u0aZPuvPNOZWdnKy4uTu+8884FX/PRRx9p8ODBSk5O1m9+8xstW7bsvH3a0rkwGmvQ1s6F0fo+OKvF50PXgaxatcolJSW5119/3e3evduNHz/epaenu+rq6kb3nz59usvOznbr1q1zX375pXv55ZddSkqK2759e7OPGWvRWIP8/Hy3dOlS9/nnn7udO3e622+/3fXq1csdP368tabVZNGY/1lbt251vXv3dldffbWbNGlSlGfSfNFYgyNHjrjLL7/c3XfffW7Lli3u4MGD7oMPPnD//ve/W2taYYnGGsyZM8dlZGS4tWvXuq+++sqtXr3ade3a1b344outNa0m+/vf/+4ee+wx9/bbbztJbs2aNb+6/8GDB13nzp3d1KlT3Z49e9zChQtdQkKCW79+fWiftnYujMYatKVzoXPRWYOzInE+7FCBct1117mioqLQv+vq6lx2drYrLS1tdP8ePXq4l156qcG2u+66yxUWFjb7mLEWjTU4V01NjZPkNm7cGJlBR1C05n/s2DHXp08fV1ZW5m666SbTgRKNNXj00UfdjTfeGJ0BR0E01qCgoMA98MADv7qPRU35wTR9+nR31VVXNdh2zz33uPz8/NC/29q58JcitQbnsnwuPFck1yBS58MO8yueU6dOqbKyUnl5eaFt8fHxysvLU0VFRaOvqa2tVUpKSoNtqamp+vjjj5t9zFiKxho0JhAISJK6d+8egVFHTjTnX1RUpIKCggbHtihaa/Duu+9q6NChuvvuu5WZmalBgwbp1Vdfjc4kWihaa3DDDTeovLxc+/fvlyR99tln+vjjj3XbbbdFYRatq6Ki4rzv7fz8/NB6tbVzYXNcaA0aY/Vc2FxNXYNInQ87TKD8+OOPqqurO+9OtV6vV36/v9HX5Ofna/78+Tpw4IDq6+tVVlamt99+W99//32zjxlL0ViDc9XX12vy5MkaPny4+vfvH/E5tES05r9q1Spt375dpaWlUR1/JERrDQ4ePKjFixerT58++uCDDzRx4kQ99NBDWr58eVTn0xzRWoOSkhKNHj1affv2VadOnTRo0CBNnjxZhYWFUZ1Pa/D7/Y2uVzAY1H/+8582dy5sjgutwbksnwubqylrEMnzYYcJlOZ48cUX1adPH/Xt21dJSUkqLi7W/fffr/j4jrNs4a5BUVGRPv/8c61ataqVRxodF5p/VVWVJk2apBUrVpz3/7Dbi6Z8D9TX12vw4MF65plnNGjQIE2YMEHjx4/XkiVLYjjyyGnKGrz55ptasWKFVq5cqe3bt2v58uV67rnnTEYaoq+9nQubItLnww7zk/biiy9WQkKCqqurG2yvrq5WVlZWo6+55JJL9M477+jEiRP65ptv9MUXX6hr16664oormn3MWIrGGvxScXGx1q5dqw8//FCXXXZZVObQEtGYf2VlpWpqajR48GAlJiYqMTFRGzdu1IIFC5SYmKi6urqozysc0foe6NGjh3Jzcxu8rl+/fjp06FDkJ9FC0VqDadOmha6iDBgwQGPGjNGUKVPaxJW1C8nKymp0vTwej1JTU9vcubA5LrQGv2T9XNhcF1qDSJ8PO0ygJCUlaciQISovLw9tq6+vV3l5uXw+36++NiUlRZdeeqnOnDmjt956S7///e9bfMxYiMYaSJJzTsXFxVqzZo02bNignJycqM2hJaIx/xEjRmjXrl3auXNn6DF06FAVFhZq586dSkhIiOqcwhWt74Hhw4ef93HK/fv36/LLL4/sBCIgWmvw888/n3dlMSEhQfX19ZGdQAz4fL4G6yVJZWVlofVqa+fC5rjQGkht51zYXBdag4ifD5v11to2atWqVS45OdktW7bM7dmzx02YMMGlp6c7v9/vnHNuzJgxrqSkJLT/5s2b3VtvveW+/PJLt2nTJnfzzTe7nJwc99NPPzX5mNZEYw0mTpzo0tLS3EcffeS+//770OPnn39u7eldUDTmfy7rn+KJxhps3brVJSYmujlz5rgDBw64FStWuM6dO7s33nijtafXJNFYg7Fjx7pLL7009DHjt99+21188cVu+vTprT29Czp27JjbsWOH27Fjh5Pk5s+f73bs2OG++eYb55xzJSUlbsyYMaH9z368dNq0aW7v3r1u0aJFjX7MuC2dC6OxBm3pXOhcdNbgXC05H3aoQHHOuYULF7pevXq5pKQkd91117nNmzeHnrvpppvc2LFjQ//+6KOPXL9+/VxycrLLyMhwY8aMcd99911Yx7Qo0msgqdHH0qVLW2lG4YnG98AvWQ8U56KzBu+9957r37+/S05Odn379nWvvPJKa0yl2SK9BsFg0E2aNMn16tXLpaSkuCuuuMI99thjrra2trWm1GQffvhho//Nnp3z2LFj3U033XTeawYOHOiSkpLcFVdc0eh/323pXBiNNWhr58JofR/8UkvOh3HOGbzNIQAA6NA6zHtQAABA20GgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADM+V9LRPsBpVGorwAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -236,7 +239,7 @@
     "\n",
     "Open the config and specify the starting structure and simulation parameters.\n",
     "If you specify the data set file itself, the first structure of the data set is going to be used as the initial structure.\n",
-    "Your `md_config_minimal.yaml` should look similar to this:\n",
+    "Your `md_config.yaml` should look similar to this:\n",
     "\n",
     "```yaml\n",
     "ensemble:\n",
@@ -266,7 +269,10 @@
     "    ),  # if the model from example 01 is used change this\n",
     "    \"duration\": 5000,  # fs\n",
     "    \"ensemble\": {\n",
-    "        \"temperature\": 300,\n",
+    "        \"temperature_schedule\": {\n",
+    "            \"T0\": 300,\n",
+    "            'name': \"constant\",\n",
+    "            },\n",
     "    },\n",
     "}\n",
     "config_dict = mod_config(md_config_path, config_updates)\n",
@@ -319,22 +325,86 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 21:44:19 | reading structure\n",
-      "INFO | 21:44:19 | Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n",
-      "INFO | 21:44:19 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
-      "INFO | 21:44:20 | initializing model\n",
-      "INFO | 21:44:20 | loading checkpoint from /home/linux3_i1/segreto/uni/dev/apax/examples/project/models/etoh_md/best\n",
-      "INFO | 21:44:20 | Initializing new trajectory file at md/md.h5\n",
-      "INFO | 21:44:20 | initializing simulation\n",
-      "INFO | 21:44:23 | running simulation for 5.0 ps\n",
-      "Simulation:   0%|                                                         | 0/10000 [00:00<?, ?it/s]INFO | 21:44:23 | get_compile_options: no backend supplied; disabling XLA-AutoFDO profile\n",
-      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:20<00:00, 495.87it/s, T=319.8 K]\n",
-      "INFO | 21:44:47 | simulation finished after elapsed time: 24.48 s\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732124446.453100  458649 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732124446.456299  458649 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 17:40:48 | reading structure\n",
+      "INFO | 17:40:48 | Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
+      "INFO | 17:40:48 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
+      "INFO | 17:40:49 | initializing model\n",
+      "INFO | 17:40:49 | loading checkpoint from /SCR1/segreto/dev/apax/examples/project/models/etoh_md/best\n",
+      "INFO | 17:40:49 | Building Standard model\n",
+      "INFO | 17:40:49 | initializing simulation\n",
+      "INFO | 17:40:57 | running simulation for 5.0 ps\n",
+      "Simulation:   5%|█▊                                 | 500/10000 [00:02<00:54, 174.57it/s, T=151.1 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 94679.55it/s]\u001b[A\n",
+      "\n",
+      "Creating groups: 100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1375.45it/s]\u001b[A\n",
+      "\n",
+      "Creating observables: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1709.87it/s]\u001b[A\n",
+      "Simulation:  15%|█████                             | 1500/10000 [00:04<00:18, 453.30it/s, T=182.4 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106535.53it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1104.29it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1354.75it/s]\u001b[A\n",
+      "Simulation:  25%|████████▌                         | 2500/10000 [00:05<00:11, 632.12it/s, T=468.3 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 82727.89it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1115.86it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1358.70it/s]\u001b[A\n",
+      "Simulation:  35%|███████████▉                      | 3500/10000 [00:06<00:08, 733.60it/s, T=213.5 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 111372.92it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1113.37it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1379.71it/s]\u001b[A\n",
+      "Simulation:  45%|███████████████▎                  | 4500/10000 [00:07<00:06, 787.25it/s, T=368.6 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 110696.86it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1108.43it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1381.98it/s]\u001b[A\n",
+      "Simulation:  55%|██████████████████▋               | 5500/10000 [00:08<00:05, 814.36it/s, T=549.4 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 79769.95it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1104.00it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1374.28it/s]\u001b[A\n",
+      "Simulation:  65%|██████████████████████            | 6500/10000 [00:10<00:04, 828.10it/s, T=277.2 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 116025.01it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1113.32it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1369.35it/s]\u001b[A\n",
+      "Simulation:  75%|█████████████████████████▌        | 7500/10000 [00:11<00:02, 834.13it/s, T=303.9 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 110726.08it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1106.68it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1351.69it/s]\u001b[A\n",
+      "Simulation:  85%|████████████████████████████▉     | 8500/10000 [00:12<00:01, 837.66it/s, T=332.7 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 78208.17it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1110.78it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1381.07it/s]\u001b[A\n",
+      "Simulation:  95%|████████████████████████████████▎ | 9500/10000 [00:13<00:00, 839.11it/s, T=288.9 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 112357.46it/s]\u001b[A\n",
+      "\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1109.02it/s]\u001b[A\n",
+      "\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1365.78it/s]\u001b[A\n",
+      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:14<00:00, 705.17it/s, T=461.2 K]\n",
+      "WARNING | 17:41:11 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "INFO | 17:41:12 | simulation finished after: 14.24 s\n",
+      "INFO | 17:41:12 | performance summary: 30.34 ns/day, 158.23 mu s/step/atom\n"
      ]
     }
    ],
    "source": [
-    "!apax md config.yaml md_config.yaml"
+    "!apax md config_full.yaml md_config.yaml"
    ]
   },
   {
@@ -352,7 +422,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -377,11 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!rm -rf project md config.yaml example.traj md_config.yaml"
+    "!rm -rf project md config_full.yaml example.traj md_config.yaml"
    ]
   },
   {
@@ -394,7 +464,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "apax",
+   "display_name": "new_defaults",
    "language": "python",
    "name": "python3"
   },
@@ -408,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/03_Transfer_Learning.ipynb b/examples/03_Transfer_Learning.ipynb
index 36c2ad6e..cda9a8cd 100644
--- a/examples/03_Transfer_Learning.ipynb
+++ b/examples/03_Transfer_Learning.ipynb
@@ -114,18 +114,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ms/miniconda3/envs/apax311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
-      "  pid, fd = os.forkpty()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!apax template train"
+    "!apax template train --full"
    ]
   },
   {
@@ -134,7 +125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 100,\n",
@@ -152,7 +143,7 @@
     "}\n",
     "config_dict = mod_config(config_path, config_updates)\n",
     "\n",
-    "with open(\"config.yaml\", \"w\") as conf:\n",
+    "with open(\"config_full.yaml\", \"w\") as conf:\n",
     "    yaml.dump(config_dict, conf, default_flow_style=False)"
    ]
   },
@@ -165,26 +156,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 16:25:57 | Running on [cuda(id=0)]\n",
-      "INFO | 16:25:57 | Initializing Callbacks\n",
-      "INFO | 16:25:57 | Initializing Loss Function\n",
-      "INFO | 16:25:57 | Initializing Metrics\n",
-      "INFO | 16:25:57 | Running Input Pipeline\n",
-      "INFO | 16:25:57 | Read data file project/benzene_mod.xyz\n",
-      "INFO | 16:25:57 | Loading data from project/benzene_mod.xyz\n",
-      "INFO | 16:26:06 | Computing per element energy regression.\n",
-      "INFO | 16:26:06 | Initializing Model\n",
-      "INFO | 16:26:06 | initializing 1 models\n",
-      "INFO | 16:26:10 | Initializing Optimizer\n",
-      "INFO | 16:26:10 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 16:26:23 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before May 1st, 2024. If your Pytree has empty ([], {}, None) values then use PyTreeCheckpointHandler(..., write_tree_metadata=True, ...) or use StandardCheckpointHandler to avoid TypeHandler Registry error. Please note that PyTreeCheckpointHandler.write_tree_metadata default value is already set to True.\n",
-      "Epochs: 100%|███████████████████████████████████| 100/100 [00:36<00:00,  2.71it/s, val_loss=0.00997]\n",
-      "INFO | 16:26:47 | Finished training\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732124930.427330  459463 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732124930.430484  459463 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 17:48:53 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 17:48:53 | Initializing Callbacks\n",
+      "INFO | 17:48:53 | Initializing Loss Function\n",
+      "INFO | 17:48:53 | Initializing Metrics\n",
+      "INFO | 17:48:53 | Running Input Pipeline\n",
+      "INFO | 17:48:53 | Reading data file project/benzene_mod.xyz\n",
+      "INFO | 17:49:00 | Found n_train: 1000, n_val: 200\n",
+      "INFO | 17:49:00 | Computing per element energy regression.\n",
+      "INFO | 17:49:01 | Building Standard model\n",
+      "INFO | 17:49:01 | initializing 1 model(s)\n",
+      "INFO | 17:49:08 | Initializing Optimizer\n",
+      "INFO | 17:49:08 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:49:16 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:52<00:00,  1.89it/s, val_loss=0.0206]\n",
+      "INFO | 17:50:01 | Finished training\n"
      ]
     }
    ],
    "source": [
-    "!apax train config.yaml"
+    "!apax train config_full.yaml"
    ]
   },
   {
@@ -203,7 +197,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 100,\n",
@@ -234,21 +228,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 16:26:51 | Running on [cuda(id=0)]\n",
-      "INFO | 16:26:51 | Initializing Callbacks\n",
-      "INFO | 16:26:51 | Initializing Loss Function\n",
-      "INFO | 16:26:51 | Initializing Metrics\n",
-      "INFO | 16:26:51 | Running Input Pipeline\n",
-      "INFO | 16:26:51 | Read data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 16:26:51 | Loading data from project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 16:26:51 | Computing per element energy regression.\n",
-      "INFO | 16:26:51 | Initializing Model\n",
-      "INFO | 16:26:51 | initializing 1 models\n",
-      "INFO | 16:26:55 | Initializing Optimizer\n",
-      "INFO | 16:26:55 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 16:27:07 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before May 1st, 2024. If your Pytree has empty ([], {}, None) values then use PyTreeCheckpointHandler(..., write_tree_metadata=True, ...) or use StandardCheckpointHandler to avoid TypeHandler Registry error. Please note that PyTreeCheckpointHandler.write_tree_metadata default value is already set to True.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:20<00:00,  4.81it/s, val_loss=0.0632]\n",
-      "INFO | 16:27:16 | Finished training\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732125003.227050  460393 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732125003.230272  460393 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 17:50:05 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 17:50:05 | Initializing Callbacks\n",
+      "INFO | 17:50:05 | Initializing Loss Function\n",
+      "INFO | 17:50:05 | Initializing Metrics\n",
+      "INFO | 17:50:05 | Running Input Pipeline\n",
+      "INFO | 17:50:05 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 17:50:05 | Found n_train: 50, n_val: 10\n",
+      "INFO | 17:50:05 | Computing per element energy regression.\n",
+      "INFO | 17:50:06 | Building Standard model\n",
+      "INFO | 17:50:06 | initializing 1 model(s)\n",
+      "INFO | 17:50:13 | Initializing Optimizer\n",
+      "INFO | 17:50:13 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:50:20 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|██████████████████████████████████████| 100/100 [00:29<00:00,  3.39it/s, val_loss=0.12]\n",
+      "INFO | 17:50:42 | Finished training\n"
      ]
     }
    ],
@@ -277,7 +274,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 100,\n",
@@ -318,30 +315,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 16:27:20 | Running on [cuda(id=0)]\n",
-      "INFO | 16:27:20 | Initializing Callbacks\n",
-      "INFO | 16:27:20 | Initializing Loss Function\n",
-      "INFO | 16:27:20 | Initializing Metrics\n",
-      "INFO | 16:27:20 | Running Input Pipeline\n",
-      "INFO | 16:27:20 | Read data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 16:27:20 | Loading data from project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 16:27:20 | Computing per element energy regression.\n",
-      "INFO | 16:27:20 | Initializing Model\n",
-      "INFO | 16:27:20 | initializing 1 models\n",
-      "INFO | 16:27:24 | Initializing Optimizer\n",
-      "INFO | 16:27:24 | loading checkpoint from project/models/benzene_dft/best\n",
-      "INFO | 16:27:24 | Transferring parameters from project/models/benzene_dft\n",
-      "INFO | 16:27:24 | Transferring parameter: radial_fn\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_0\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_0\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_1\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_1\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_2\n",
-      "INFO | 16:27:24 | Transferring parameter: dense_2\n",
-      "INFO | 16:27:24 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 16:27:36 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before May 1st, 2024. If your Pytree has empty ([], {}, None) values then use PyTreeCheckpointHandler(..., write_tree_metadata=True, ...) or use StandardCheckpointHandler to avoid TypeHandler Registry error. Please note that PyTreeCheckpointHandler.write_tree_metadata default value is already set to True.\n",
-      "Epochs: 100%|███████████████████████████████████| 100/100 [00:20<00:00,  4.91it/s, val_loss=0.00483]\n",
-      "INFO | 16:27:45 | Finished training\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732125044.359099  461322 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732125044.362317  461322 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 17:50:46 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 17:50:46 | Initializing Callbacks\n",
+      "INFO | 17:50:46 | Initializing Loss Function\n",
+      "INFO | 17:50:46 | Initializing Metrics\n",
+      "INFO | 17:50:46 | Running Input Pipeline\n",
+      "INFO | 17:50:46 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 17:50:46 | Found n_train: 50, n_val: 10\n",
+      "INFO | 17:50:46 | Computing per element energy regression.\n",
+      "INFO | 17:50:46 | Building Standard model\n",
+      "INFO | 17:50:46 | initializing 1 model(s)\n",
+      "INFO | 17:50:53 | Initializing Optimizer\n",
+      "INFO | 17:50:53 | loading checkpoint from project/models/benzene_dft/best\n",
+      "INFO | 17:50:53 | Transferring parameters from project/models/benzene_dft\n",
+      "INFO | 17:50:53 | Transferring parameter: radial_fn\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_0\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_0\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_1\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_1\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_2\n",
+      "INFO | 17:50:53 | Transferring parameter: dense_2\n",
+      "INFO | 17:50:53 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:50:59 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:25<00:00,  3.91it/s, val_loss=0.0078]\n",
+      "INFO | 17:51:19 | Finished training\n"
      ]
     }
    ],
@@ -361,7 +361,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "apax",
+   "display_name": "new_defaults",
    "language": "python",
    "name": "python3"
   },
@@ -375,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/04_Batch_Data_Selection.ipynb b/examples/04_Batch_Data_Selection.ipynb
index 6ab30370..14f3deca 100644
--- a/examples/04_Batch_Data_Selection.ipynb
+++ b/examples/04_Batch_Data_Selection.ipynb
@@ -18,7 +18,17 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732125763.498333  463862 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732125763.501418  463862 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -47,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,29 +80,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/ms/miniconda3/envs/apax311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
-      "  pid, fd = os.forkpty()\n"
+      "There is already a config file in the working directory.\n"
      ]
     }
    ],
    "source": [
-    "!apax template train"
+    "!apax template train --full"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = Path(\"config.yaml\")\n",
+    "config_path = Path(\"config_full.yaml\")\n",
     "\n",
     "config_updates = {\n",
     "    \"n_epochs\": 200,\n",
@@ -110,49 +119,75 @@
     "}\n",
     "config_dict = mod_config(config_path, config_updates)\n",
     "\n",
-    "with open(\"config.yaml\", \"w\") as conf:\n",
+    "with open(\"config_full.yaml\", \"w\") as conf:\n",
     "    yaml.dump(config_dict, conf, default_flow_style=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ms/miniconda3/envs/apax311/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
-      "  pid, fd = os.forkpty()\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO | 18:13:29 | Running on [cuda(id=0)]\n",
-      "INFO | 18:13:29 | Initializing Callbacks\n",
-      "INFO | 18:13:29 | Initializing Loss Function\n",
-      "INFO | 18:13:29 | Initializing Metrics\n",
-      "INFO | 18:13:29 | Running Input Pipeline\n",
-      "INFO | 18:13:29 | Read training data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 18:13:29 | Read validation data file project/benzene_ccsd_t-test_mod.xyz\n",
-      "INFO | 18:13:29 | Loading data from project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 18:13:29 | Loading data from project/benzene_ccsd_t-test_mod.xyz\n",
-      "INFO | 18:13:30 | Computing per element energy regression.\n",
-      "INFO | 18:13:30 | Initializing Model\n",
-      "INFO | 18:13:30 | initializing 1 models\n",
-      "INFO | 18:13:34 | Initializing Optimizer\n",
-      "INFO | 18:13:34 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/200 [00:00<?, ?it/s]WARNING | 18:13:47 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before May 1st, 2024. If your Pytree has empty ([], {}, None) values then use PyTreeCheckpointHandler(..., write_tree_metadata=True, ...) or use StandardCheckpointHandler to avoid TypeHandler Registry error. Please note that PyTreeCheckpointHandler.write_tree_metadata default value is already set to True.\n",
-      "Epochs: 100%|██████████████████████████████████| 200/200 [01:31<00:00,  2.18it/s, val_loss=0.000692]\n",
-      "INFO | 18:15:06 | Finished training\n"
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1732125768.132223  463950 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732125768.135407  463950 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 18:02:49 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 18:02:49 | Initializing Callbacks\n",
+      "INFO | 18:02:49 | Initializing Loss Function\n",
+      "INFO | 18:02:49 | Initializing Metrics\n",
+      "INFO | 18:02:49 | Running Input Pipeline\n",
+      "INFO | 18:02:49 | Reading training data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 18:02:49 | Reading validation data file project/benzene_ccsd_t-test_mod.xyz\n",
+      "INFO | 18:02:50 | Found n_train: 1000, n_val: 500\n",
+      "INFO | 18:02:50 | Computing per element energy regression.\n",
+      "INFO | 18:02:51 | Building Standard model\n",
+      "INFO | 18:02:51 | initializing 1 model(s)\n",
+      "INFO | 18:02:57 | Initializing Optimizer\n",
+      "INFO | 18:02:57 | Beginning Training\n",
+      "INFO | 18:02:57 | Loading checkpoint\n",
+      "WARNING | 18:02:57 | The transformations API will eventually be replaced by an upgraded design. The current API will not be removed until this point, but it will no longer be actively worked on.\n",
+      "INFO | 18:02:57 | Successfully restored checkpoint from epoch 199\n",
+      "\u001b[31m╭─\u001b[0m\u001b[31m────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m─────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/cli/\u001b[0m\u001b[1;33mapax_app.py\u001b[0m:\u001b[94m46\u001b[0m in \u001b[92mtrain\u001b[0m                      \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 43 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 44 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mfrom\u001b[0m \u001b[4;96mapax\u001b[0m\u001b[4;96m.\u001b[0m\u001b[4;96mtrain\u001b[0m\u001b[4;96m.\u001b[0m\u001b[4;96mrun\u001b[0m \u001b[94mimport\u001b[0m run                                     \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 45 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 46 \u001b[2m│   \u001b[0mrun(train_config_path, log_level)                                  \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 47 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 48 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 49 \u001b[0m\u001b[1;95m@app\u001b[0m.command()                                                         \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/train/\u001b[0m\u001b[1;33mrun.py\u001b[0m:\u001b[94m206\u001b[0m in \u001b[92mrun\u001b[0m                          \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m203 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94melse\u001b[0m:                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m204 \u001b[0m\u001b[2m│   │   \u001b[0mema_handler = \u001b[94mNone\u001b[0m                                             \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m205 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m206 \u001b[2m│   \u001b[0mfit(                                                               \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m207 \u001b[0m\u001b[2m│   │   \u001b[0mstate,                                                         \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m208 \u001b[0m\u001b[2m│   │   \u001b[0mtrain_ds,                                                      \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m209 \u001b[0m\u001b[2m│   │   \u001b[0mloss_fn,                                                       \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/train/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m90\u001b[0m in \u001b[92mfit\u001b[0m                       \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 87 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 88 \u001b[0m\u001b[2m│   \u001b[0mstate, start_epoch = load_state(state, latest_dir)                 \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 89 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m start_epoch >= n_epochs:                                        \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 90 \u001b[2m│   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 91 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mn_epochs <= current epoch from checkpoint (\u001b[0m\u001b[33m{\u001b[0mn_epochs\u001b[33m}\u001b[0m\u001b[33m <=\u001b[0m \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 92 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                              \u001b[31m│\u001b[0m\n",
+      "\u001b[31m│\u001b[0m   \u001b[2m 93 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
+      "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
+      "\u001b[1;91mValueError: \u001b[0mn_epochs <= current epoch from checkpoint \u001b[1m(\u001b[0m\u001b[1;36m200\u001b[0m <= \u001b[1;36m200\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "!apax train config.yaml"
+    "!apax train config_full.yaml"
    ]
   },
   {
@@ -167,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,66 +213,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy per atom: Epot = -524.783eV  Ekin = 0.042eV (T=322K)  Etot = -524.741eV\n",
-      "Energy per atom: Epot = -524.784eV  Ekin = 0.032eV (T=246K)  Etot = -524.752eV\n",
-      "Energy per atom: Epot = -524.786eV  Ekin = 0.037eV (T=287K)  Etot = -524.749eV\n",
-      "Energy per atom: Epot = -524.784eV  Ekin = 0.033eV (T=252K)  Etot = -524.752eV\n",
-      "Energy per atom: Epot = -524.786eV  Ekin = 0.040eV (T=307K)  Etot = -524.746eV\n",
-      "Energy per atom: Epot = -524.781eV  Ekin = 0.036eV (T=275K)  Etot = -524.746eV\n",
-      "Energy per atom: Epot = -524.783eV  Ekin = 0.038eV (T=295K)  Etot = -524.745eV\n",
-      "Energy per atom: Epot = -524.777eV  Ekin = 0.031eV (T=242K)  Etot = -524.745eV\n",
-      "Energy per atom: Epot = -524.772eV  Ekin = 0.038eV (T=292K)  Etot = -524.735eV\n",
-      "Energy per atom: Epot = -524.776eV  Ekin = 0.039eV (T=305K)  Etot = -524.736eV\n",
-      "Energy per atom: Epot = -524.788eV  Ekin = 0.055eV (T=427K)  Etot = -524.733eV\n",
-      "      Step     Time          Energy         fmax\n",
-      "FIRE:    0 18:31:17    -6297.460205        2.9797\n",
-      "FIRE:    1 18:31:17    -6297.640137        0.9522\n",
-      "FIRE:    2 18:31:17    -6297.634888        2.8176\n",
-      "FIRE:    3 18:31:17    -6297.690125        2.0002\n",
-      "FIRE:    4 18:31:17    -6297.744812        0.7309\n",
-      "FIRE:    5 18:31:17    -6297.752930        1.0354\n",
-      "FIRE:    6 18:31:17    -6297.754883        0.9791\n",
-      "FIRE:    7 18:31:17    -6297.759338        0.8737\n",
-      "FIRE:    8 18:31:17    -6297.764954        0.7249\n",
-      "FIRE:    9 18:31:17    -6297.770813        0.5532\n",
-      "FIRE:   10 18:31:17    -6297.776001        0.4649\n",
-      "FIRE:   11 18:31:17    -6297.780212        0.4568\n",
-      "FIRE:   12 18:31:17    -6297.783813        0.4434\n",
-      "FIRE:   13 18:31:17    -6297.788879        0.5014\n",
-      "FIRE:   14 18:31:17    -6297.794434        0.5313\n",
-      "FIRE:   15 18:31:17    -6297.801697        0.4645\n",
-      "FIRE:   16 18:31:17    -6297.810730        0.3380\n",
-      "FIRE:   17 18:31:17    -6297.818176        0.2703\n",
-      "FIRE:   18 18:31:17    -6297.823425        0.2700\n",
-      "FIRE:   19 18:31:17    -6297.829163        0.3942\n",
-      "FIRE:   20 18:31:17    -6297.834229        0.3305\n",
-      "FIRE:   21 18:31:17    -6297.838928        0.2279\n",
-      "FIRE:   22 18:31:17    -6297.841248        0.3689\n",
-      "FIRE:   23 18:31:17    -6297.845642        0.3514\n",
-      "FIRE:   24 18:31:17    -6297.849121        0.2314\n",
-      "FIRE:   25 18:31:17    -6297.850342        0.3102\n",
-      "FIRE:   26 18:31:17    -6297.850586        0.2551\n",
-      "FIRE:   27 18:31:17    -6297.851562        0.1678\n",
-      "FIRE:   28 18:31:17    -6297.852051        0.1254\n",
-      "FIRE:   29 18:31:17    -6297.852356        0.1746\n",
-      "FIRE:   30 18:31:17    -6297.853149        0.2219\n",
-      "FIRE:   31 18:31:17    -6297.853760        0.2102\n",
-      "FIRE:   32 18:31:17    -6297.854919        0.1454\n",
-      "FIRE:   33 18:31:17    -6297.855469        0.0630\n",
-      "FIRE:   34 18:31:17    -6297.856812        0.1301\n",
-      "FIRE:   35 18:31:17    -6297.857239        0.1528\n",
-      "FIRE:   36 18:31:17    -6297.857483        0.0839\n",
-      "FIRE:   37 18:31:17    -6297.857483        0.0878\n",
-      "FIRE:   38 18:31:17    -6297.857605        0.0763\n",
-      "FIRE:   39 18:31:17    -6297.857544        0.0549\n",
-      "FIRE:   40 18:31:17    -6297.857056        0.0411\n"
+      "Energy per atom: Epot = -524.783eV  Ekin = 0.035eV (T=273K)  Etot = -524.747eV\n",
+      "Energy per atom: Epot = -524.784eV  Ekin = 0.039eV (T=303K)  Etot = -524.745eV\n",
+      "Energy per atom: Epot = -524.789eV  Ekin = 0.040eV (T=310K)  Etot = -524.749eV\n",
+      "Energy per atom: Epot = -524.786eV  Ekin = 0.037eV (T=285K)  Etot = -524.749eV\n",
+      "Energy per atom: Epot = -524.792eV  Ekin = 0.039eV (T=305K)  Etot = -524.753eV\n",
+      "Energy per atom: Epot = -524.778eV  Ekin = 0.039eV (T=301K)  Etot = -524.739eV\n",
+      "Energy per atom: Epot = -524.780eV  Ekin = 0.053eV (T=413K)  Etot = -524.727eV\n",
+      "Energy per atom: Epot = -524.771eV  Ekin = 0.046eV (T=353K)  Etot = -524.726eV\n",
+      "Energy per atom: Epot = -524.775eV  Ekin = 0.051eV (T=393K)  Etot = -524.724eV\n",
+      "Energy per atom: Epot = -524.776eV  Ekin = 0.049eV (T=382K)  Etot = -524.726eV\n",
+      "Energy per atom: Epot = -524.763eV  Ekin = 0.039eV (T=301K)  Etot = -524.724eV\n",
+      "      Step     Time          Energy          fmax\n",
+      "FIRE:    0 18:03:10    -6297.160709        4.643667\n",
+      "FIRE:    1 18:03:10    -6297.568402        1.215557\n",
+      "FIRE:    2 18:03:10    -6297.416246        3.988612\n",
+      "FIRE:    3 18:03:10    -6297.546911        2.973441\n",
+      "FIRE:    4 18:03:10    -6297.678424        1.334519\n",
+      "FIRE:    5 18:03:10    -6297.695949        1.463297\n",
+      "FIRE:    6 18:03:10    -6297.700549        1.363753\n",
+      "FIRE:    7 18:03:10    -6297.708687        1.175066\n",
+      "FIRE:    8 18:03:10    -6297.718344        0.913409\n",
+      "FIRE:    9 18:03:10    -6297.727659        0.727679\n",
+      "FIRE:   10 18:03:10    -6297.736589        0.619541\n",
+      "FIRE:   11 18:03:10    -6297.742862        0.511749\n",
+      "FIRE:   12 18:03:10    -6297.748436        0.697062\n",
+      "FIRE:   13 18:03:10    -6297.755050        0.857826\n",
+      "FIRE:   14 18:03:10    -6297.763414        0.924485\n",
+      "FIRE:   15 18:03:10    -6297.775700        0.863180\n",
+      "FIRE:   16 18:03:10    -6297.790781        0.655139\n",
+      "FIRE:   17 18:03:10    -6297.805233        0.349130\n",
+      "FIRE:   18 18:03:10    -6297.816560        0.393361\n",
+      "FIRE:   19 18:03:10    -6297.826240        0.583213\n",
+      "FIRE:   20 18:03:10    -6297.836858        0.470288\n",
+      "FIRE:   21 18:03:10    -6297.846426        0.208600\n",
+      "FIRE:   22 18:03:10    -6297.847673        0.452071\n",
+      "FIRE:   23 18:03:10    -6297.849254        0.408002\n",
+      "FIRE:   24 18:03:10    -6297.850233        0.325805\n",
+      "FIRE:   25 18:03:10    -6297.851149        0.224788\n",
+      "FIRE:   26 18:03:10    -6297.852456        0.150222\n",
+      "FIRE:   27 18:03:10    -6297.852044        0.165351\n",
+      "FIRE:   28 18:03:10    -6297.852139        0.215623\n",
+      "FIRE:   29 18:03:10    -6297.851961        0.243710\n",
+      "FIRE:   30 18:03:10    -6297.852954        0.222756\n",
+      "FIRE:   31 18:03:10    -6297.854113        0.148616\n",
+      "FIRE:   32 18:03:10    -6297.855736        0.084148\n",
+      "FIRE:   33 18:03:10    -6297.855055        0.126039\n",
+      "FIRE:   34 18:03:10    -6297.855310        0.187920\n",
+      "FIRE:   35 18:03:10    -6297.856459        0.159655\n",
+      "FIRE:   36 18:03:10    -6297.856218        0.078716\n",
+      "FIRE:   37 18:03:10    -6297.857179        0.153488\n",
+      "FIRE:   38 18:03:10    -6297.857669        0.133507\n",
+      "FIRE:   39 18:03:10    -6297.857858        0.097674\n",
+      "FIRE:   40 18:03:10    -6297.857554        0.054184\n",
+      "FIRE:   41 18:03:10    -6297.857677        0.051685\n",
+      "FIRE:   42 18:03:10    -6297.858031        0.076014\n",
+      "FIRE:   43 18:03:10    -6297.858488        0.095619\n",
+      "FIRE:   44 18:03:10    -6297.857724        0.098065\n",
+      "FIRE:   45 18:03:10    -6297.857533        0.081023\n",
+      "FIRE:   46 18:03:10    -6297.858317        0.043732\n"
      ]
     },
     {
@@ -246,7 +287,7 @@
        "True"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -287,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,16 +338,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "142"
+       "148"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -317,28 +358,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing features: 100%|██████████████████████████████████████| 1142/1142 [00:04<00:00, 273.60it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1142, 513)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "Computing features: 100%|██████████████████████████████████████| 1148/1148 [00:02<00:00, 410.32it/s]\n"
      ]
     }
    ],
@@ -361,16 +388,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([139,  99,   1,  34, 102,  78,   4,  17,  97,  72])"
+       "(array([145,  99,  13,  28, 105,   8,   7,   3,  34,  77]),\n",
+       " array([2.1153207e+04, 8.0031250e+01, 1.9972656e+01, 5.7343750e+00,\n",
+       "        5.0390625e+00, 2.3984375e+00, 2.2890625e+00, 2.0976562e+00,\n",
+       "        1.5156250e+00, 1.2968750e+00], dtype=float32),\n",
+       " array([[-8.0443577e-08, -3.9290704e-08, -5.0462347e-07, ...,\n",
+       "         -2.8600613e-07, -2.1077530e-12,  1.0578943e+01],\n",
+       "        [-7.8728974e-08, -3.6814725e-08, -4.9945356e-07, ...,\n",
+       "         -2.7572671e-07, -2.1329063e-12,  1.0578943e+01],\n",
+       "        [-7.7208618e-08, -3.9166920e-08, -4.5074782e-07, ...,\n",
+       "         -2.8476998e-07, -2.1711810e-12,  1.0578943e+01],\n",
+       "        ...,\n",
+       "        [-7.8029473e-08, -3.7501913e-08, -5.0669252e-07, ...,\n",
+       "         -2.7671780e-07, -2.1255159e-12,  1.0578943e+01],\n",
+       "        [-7.9816232e-08, -3.9418715e-08, -4.7391208e-07, ...,\n",
+       "         -2.8574479e-07, -2.1665530e-12,  1.0578943e+01],\n",
+       "        [-7.9606153e-08, -3.6622986e-08, -5.3845122e-07, ...,\n",
+       "         -2.7972229e-07, -2.0286058e-12,  1.0578943e+01]], dtype=float32),\n",
+       " array([[-8.0443577e-08, -3.9290704e-08, -5.0462347e-07, ...,\n",
+       "         -2.8600633e-07, -2.1077530e-12,  1.0578943e+01],\n",
+       "        [-7.6723715e-08, -4.3407518e-08, -3.9524843e-07, ...,\n",
+       "         -2.9376406e-07, -2.1438441e-12,  1.0578943e+01],\n",
+       "        [-7.1703973e-08, -2.9766772e-08, -4.5751801e-07, ...,\n",
+       "         -2.5320008e-07, -1.6724249e-12,  1.0578943e+01],\n",
+       "        ...,\n",
+       "        [-8.3775660e-08, -4.6998672e-08, -4.5863644e-07, ...,\n",
+       "         -3.1039335e-07, -2.4709093e-12,  1.0578943e+01],\n",
+       "        [-8.3781032e-08, -4.7010957e-08, -4.5846181e-07, ...,\n",
+       "         -3.1048981e-07, -2.4710767e-12,  1.0578943e+01],\n",
+       "        [-8.3768818e-08, -4.7006104e-08, -4.5847912e-07, ...,\n",
+       "         -3.1042489e-07, -2.4719324e-12,  1.0578943e+01]], dtype=float32))"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -381,13 +437,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "energies = np.array([a.get_potential_energy() for a in pool_atoms])\n",
     "# selected_indices = np.random.randint(0, len(energies), 10)\n",
-    "selection_energies = energies[selected_indices]"
+    "selection_energies = energies[selected_indices[0]]"
    ]
   },
   {
@@ -400,22 +456,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x79751d048c50>"
+       "<matplotlib.legend.Legend at 0x7efe235b8e90>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -429,17 +485,24 @@
     "\n",
     "ax.plot(energies)\n",
     "ax.scatter(\n",
-    "    selected_indices, selection_energies, marker=\"x\", color=\"red\", label=\"selection\"\n",
+    "    selected_indices[0], selection_energies, marker=\"x\", color=\"red\", label=\"selection\"\n",
     ")\n",
     "ax.set_ylabel(\"Energy / eV\")\n",
     "ax.set_xlabel(\"Image\")\n",
     "ax.legend()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "apax311",
+   "display_name": "new_defaults",
    "language": "python",
    "name": "python3"
   },
@@ -453,7 +516,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

From bd0d1845690a264a2468fd320565365390739160 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:10:49 +0000
Subject: [PATCH 05/17] config updates

---
 apax/cli/templates/train_config_full.yaml | 40 ++++++++++++++---------
 apax/config/lr_config.py                  |  2 +-
 apax/config/md_config.py                  |  2 +-
 apax/config/model_config.py               |  2 +-
 4 files changed, 27 insertions(+), 19 deletions(-)

diff --git a/apax/cli/templates/train_config_full.yaml b/apax/cli/templates/train_config_full.yaml
index 13d89656..dadc6d3d 100644
--- a/apax/cli/templates/train_config_full.yaml
+++ b/apax/cli/templates/train_config_full.yaml
@@ -24,7 +24,7 @@ data:
   n_train: 1000
   n_valid: 100
 
-  batch_size: 32
+  batch_size: 8
   valid_batch_size: 100
 
   shift_method: "per_element_regression_shift"
@@ -38,11 +38,17 @@ data:
 
 model:
   name: gmnn
+  use_ntk: false
   basis:
-    name: gaussian
-    n_basis: 7
-    r_max: 6.0
-    r_min: 0.5
+    name: bessel
+    n_basis: 16
+    r_max: 6.5
+
+  # if you would like to use emirical repulsion corrections
+  # with the following example.
+  # empirical_corrections:
+  #   - name: exponential
+  #     r_max: 1.5
 
   ensemble: null
   # if you would like to train model ensembles, this can be achieved with
@@ -53,15 +59,15 @@ model:
 
   n_radial: 5
   n_contr: 8
-  nn: [512, 512]
+  nn: [256, 256]
 
-  calc_stress: true
+  calc_stress: false
 
-  w_init: normal
+  w_init: lecun
   b_init: zeros
-  descriptor_dtype: fp64
+  descriptor_dtype: fp32
   readout_dtype: fp32
-  scale_shift_dtype: fp32
+  scale_shift_dtype: fp64
   emb_init: uniform
 
 loss:
@@ -86,15 +92,17 @@ metrics:
 optimizer:
   name: adam
   kwargs: {}
-  emb_lr: 0.03
-  nn_lr: 0.03
+  emb_lr: 0.01
+  nn_lr: 0.01
   scale_lr: 0.001
-  shift_lr: 0.05
+  shift_lr: 0.03
   zbl_lr: 0.001
   schedule:
-    name: linear
-    transition_begin: 0
-    end_value: 1e-6
+    name: cyclic_cosine
+    period: 20
+    decay_factor: 0.90
+
+
 callbacks:
 - name: csv
 
diff --git a/apax/config/lr_config.py b/apax/config/lr_config.py
index 23791f2b..23ccc389 100644
--- a/apax/config/lr_config.py
+++ b/apax/config/lr_config.py
@@ -42,4 +42,4 @@ class CyclicCosineLR(LRSchedule, frozen=True, extra="forbid"):
 
     name: Literal["cyclic_cosine"]
     period: int = 20
-    decay_factor: NonNegativeFloat = 1.0
+    decay_factor: NonNegativeFloat = 0.90
diff --git a/apax/config/md_config.py b/apax/config/md_config.py
index aa1f209b..b93451ce 100644
--- a/apax/config/md_config.py
+++ b/apax/config/md_config.py
@@ -275,7 +275,7 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
     duration: PositiveFloat
     n_inner: PositiveInt = 100
     sampling_rate: PositiveInt = 10
-    buffer_size: PositiveInt = 100
+    buffer_size: PositiveInt = 5000
     dr_threshold: PositiveFloat = 0.5
     extra_capacity: NonNegativeInt = 0
 
diff --git a/apax/config/model_config.py b/apax/config/model_config.py
index 4ac054fd..5c4d495d 100644
--- a/apax/config/model_config.py
+++ b/apax/config/model_config.py
@@ -42,7 +42,7 @@ class BesselBasisConfig(BaseModel, extra="forbid"):
     """
 
     name: Literal["bessel"] = "bessel"
-    n_basis: PositiveInt = 7
+    n_basis: PositiveInt = 16
     r_max: PositiveFloat = 6.0
 
 

From 2d3f5d40ea8f41917b9c99a7deab2e995686f363 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Wed, 20 Nov 2024 18:11:10 +0000
Subject: [PATCH 06/17] idk

---
 apax/nodes/optimizer/__init__.py      |   3 +
 apax/nodes/optimizer/get_optimizer.py | 157 ++++++++++++++++++++++++++
 apax/nodes/optimizer/optimizers.py    |  84 ++++++++++++++
 3 files changed, 244 insertions(+)
 create mode 100644 apax/nodes/optimizer/__init__.py
 create mode 100644 apax/nodes/optimizer/get_optimizer.py
 create mode 100644 apax/nodes/optimizer/optimizers.py

diff --git a/apax/nodes/optimizer/__init__.py b/apax/nodes/optimizer/__init__.py
new file mode 100644
index 00000000..8d73c6f6
--- /dev/null
+++ b/apax/nodes/optimizer/__init__.py
@@ -0,0 +1,3 @@
+from apax.optimizer.get_optimizer import get_opt
+
+__all__ = ["get_opt"]
diff --git a/apax/nodes/optimizer/get_optimizer.py b/apax/nodes/optimizer/get_optimizer.py
new file mode 100644
index 00000000..d25d2ba0
--- /dev/null
+++ b/apax/nodes/optimizer/get_optimizer.py
@@ -0,0 +1,157 @@
+import logging
+
+import jax.numpy as jnp
+import numpy as np
+import optax
+from flax import traverse_util
+from flax.core.frozen_dict import freeze
+from optax._src import base
+
+from apax.optimizer.optimizers import ademamix, sam
+
+log = logging.getLogger(__name__)
+
+
+def cyclic_cosine_decay_schedule(
+    init_value: float,
+    steps_per_epoch,
+    period: int,
+    decay_factor: float = 0.9,
+) -> base.Schedule:
+    r"""Returns a function which implements cyclic cosine learning rate decay.
+
+    Args:
+        init_value: An initial value for the learning rate.
+
+    Returns:
+        schedule
+        A function that maps step counts to values.
+    """
+
+    def schedule(count):
+        cycle = count // (period * steps_per_epoch)
+        step_in_period = jnp.mod(count, period * steps_per_epoch)
+        arg = np.pi * step_in_period / (period * steps_per_epoch)
+        lr = init_value / 2 * (jnp.cos(arg) + 1)
+        lr = lr * (decay_factor**cycle)
+        return lr
+
+    return schedule
+
+
+def get_schedule(
+    lr: float,
+    n_epochs: int,
+    steps_per_epoch: int,
+    schedule_kwargs: dict,
+) -> optax._src.base.Schedule:
+    """
+    builds a linear learning rate schedule.
+    """
+    schedule_kwargs = schedule_kwargs.copy()
+    name = schedule_kwargs.pop("name")
+    if name == "linear":
+        lr_schedule = optax.linear_schedule(
+            init_value=lr, transition_steps=n_epochs * steps_per_epoch, **schedule_kwargs
+        )
+    elif name == "cyclic_cosine":
+        lr_schedule = cyclic_cosine_decay_schedule(lr, steps_per_epoch, **schedule_kwargs)
+    else:
+        raise KeyError(f"unknown learning rate schedule: {name}")
+    return lr_schedule
+
+
+class OptimizerFactory:
+    def __init__(
+        self, opt, n_epochs, steps_per_epoch, gradient_clipping, kwargs, schedule
+    ) -> None:
+        self.opt = opt
+        self.n_epochs = n_epochs
+        self.steps_per_epoch = steps_per_epoch
+        self.gradient_clipping = gradient_clipping
+        self.kwargs = kwargs
+        self.schedule = schedule
+
+    def create(self, lr):
+        if lr <= 1e-7:
+            optimizer = optax.set_to_zero()
+        else:
+            schedule = get_schedule(
+                lr, self.n_epochs, self.steps_per_epoch, self.schedule
+            )
+            optimizer = optax.chain(
+                optax.clip(self.gradient_clipping),
+                self.opt(schedule, **self.kwargs),
+                optax.zero_nans(),
+            )
+        return optimizer
+
+
+def get_opt(
+    params,
+    n_epochs: int,
+    steps_per_epoch: int,
+    emb_lr: float = 0.02,
+    nn_lr: float = 0.03,
+    scale_lr: float = 0.001,
+    shift_lr: float = 0.05,
+    zbl_lr: float = 0.001,
+    rep_scale_lr: float = 0.001,
+    rep_prefactor_lr: float = 0.0001,
+    gradient_clipping=1000.0,
+    name: str = "adam",
+    kwargs: dict = {},
+    schedule: dict = {},
+) -> optax._src.base.GradientTransformation:
+    """
+    Builds an optimizer with different learning rates for each parameter group.
+    Several `optax` optimizers are supported.
+    """
+
+    log.info("Initializing Optimizer")
+    if name == "sam":
+        opt = sam
+    elif name == "ademamix":
+        opt = ademamix
+    else:
+        opt = getattr(optax, name)
+
+    opt_fac = OptimizerFactory(
+        opt, n_epochs, steps_per_epoch, gradient_clipping, kwargs, schedule
+    )
+
+    nn_opt = opt_fac.create(nn_lr)
+    emb_opt = opt_fac.create(emb_lr)
+    scale_opt = opt_fac.create(scale_lr)
+    shift_opt = opt_fac.create(shift_lr)
+    zbl_opt = opt_fac.create(zbl_lr)
+    rep_scale_opt = opt_fac.create(rep_scale_lr)
+    rep_prefactor_opt = opt_fac.create(rep_prefactor_lr)
+
+    partition_optimizers = {
+        "w": nn_opt,
+        "b": nn_opt,
+        "atomic_type_embedding": emb_opt,
+        "scale_per_element": scale_opt,
+        "shift_per_element": shift_opt,
+        "a_exp": zbl_opt,
+        "a_num": zbl_opt,
+        "coefficients": zbl_opt,
+        "exponents": zbl_opt,
+        "rep_scale": rep_scale_opt,
+        "rep_prefactor": rep_prefactor_opt,
+        "kernel": nn_opt,
+        "bias": nn_opt,
+        "embedding": emb_opt,
+        "weights_K": nn_opt,
+        "weights_Q": nn_opt,
+        "weights_V": nn_opt,
+        "scale": scale_opt,
+    }
+
+    param_partitions = freeze(
+        traverse_util.path_aware_map(lambda path, v: path[-1], params)
+    )
+    tx = optax.multi_transform(partition_optimizers, param_partitions)
+
+    return tx
diff --git a/apax/nodes/optimizer/optimizers.py b/apax/nodes/optimizer/optimizers.py
new file mode 100644
index 00000000..22237e22
--- /dev/null
+++ b/apax/nodes/optimizer/optimizers.py
@@ -0,0 +1,84 @@
+from typing import NamedTuple
+
+import chex
+import jax.numpy as jnp
+import optax
+from jax import tree_util as jtu
+from optax import bias_correction, contrib, update_moment, update_moment_per_elem_norm
+from optax._src import base, combine, numerics, transform
+from optax.tree_utils import tree_zeros_like
+
+
+class ScaleByAdemamixState(NamedTuple):
+    count: chex.Array
+    count_m2: chex.Array
+    m1: base.Updates
+    m2: base.Updates
+    nu: base.Updates
+
+
+def ademamix(
+    lr,
+    b1=0.9,
+    b2=0.999,
+    b3=0.9999,
+    alpha=5.0,
+    b3_scheduler=None,  # TODO maybe implement schedules
+    alpha_scheduler=None,
+    eps=1e-8,
+    weight_decay=0.0,
+):
+    """AdEMAmix implementation directly taken from the original implementation:
+    2409.03137
+    """
+    return combine.chain(
+        scale_by_ademamix(b1, b2, b3, alpha, b3_scheduler, alpha_scheduler, eps),
+        transform.add_decayed_weights(weight_decay),
+        transform.scale_by_learning_rate(lr),
+    )
+
+
+def scale_by_ademamix(b1, b2, b3, alpha, b3_scheduler, alpha_scheduler, eps):
+    def init_fn(params):
+        m1 = tree_zeros_like(params)  # fast EMA
+        m2 = tree_zeros_like(params)  # slow EMA
+        nu = tree_zeros_like(params)  # second moment estimate
+        return ScaleByAdemamixState(
+            count=jnp.zeros([], jnp.int32),
+            count_m2=jnp.zeros([], jnp.int32),
+            m1=m1,
+            m2=m2,
+            nu=nu,
+        )
+
+    def update_fn(updates, state, params=None):
+        del params
+        c_b3 = b3_scheduler(state.count_m2) if b3_scheduler is not None else b3
+        c_alpha = (
+            alpha_scheduler(state.count_m2) if alpha_scheduler is not None else alpha
+        )
+        m1 = update_moment(updates, state.m1, b1, 1)  # m1 = b1 * m1 + (1-b1) * updates
+        m2 = update_moment(updates, state.m2, c_b3, 1)
+        nu = update_moment_per_elem_norm(updates, state.nu, b2, 2)
+        count_inc = numerics.safe_int32_increment(state.count)
+        count_m2_inc = numerics.safe_int32_increment(state.count_m2)
+        m1_hat = bias_correction(m1, b1, count_inc)
+        nu_hat = bias_correction(nu, b2, count_inc)
+        updates = jtu.tree_map(
+            lambda m1_, m2_, v_: (m1_ + c_alpha * m2_) / (jnp.sqrt(v_) + eps),
+            m1_hat,
+            m2,
+            nu_hat,
+        )
+        return updates, ScaleByAdemamixState(
+            count=count_inc, count_m2=count_m2_inc, m1=m1, m2=m2, nu=nu
+        )
+
+    return base.GradientTransformation(init_fn, update_fn)
+
+
+def sam(lr=1e-3, b1=0.9, b2=0.999, rho=0.001, sync_period=2):
+    """A SAM optimizer using Adam for the outer optimizer."""
+    opt = optax.adam(lr, b1=b1, b2=b2)
+    adv_opt = optax.chain(contrib.normalize(), optax.sgd(rho))
+    return contrib.sam(opt, adv_opt, sync_period=sync_period)

From 15cc06fc7754c2171910df5f1f6ee88400e78806 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 20 Nov 2024 18:17:51 +0000
Subject: [PATCH 07/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 apax/md/simulate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apax/md/simulate.py b/apax/md/simulate.py
index 2cb7a64c..d9a23d3d 100644
--- a/apax/md/simulate.py
+++ b/apax/md/simulate.py
@@ -17,7 +17,7 @@
 from apax.config import Config, MDConfig, parse_config
 from apax.config.md_config import Integrator
 from apax.md.ase_calc import make_ensemble, maybe_vmap
-from apax.md.constraints import Constraint, ConstraintBase, FixAtoms
+from apax.md.constraints import Constraint, ConstraintBase
 from apax.md.dynamics_checks import DynamicsCheckBase, DynamicsChecks
 from apax.md.io import H5TrajHandler, TrajHandler, truncate_trajectory_to_checkpoint
 from apax.md.md_checkpoint import load_md_state

From df7639f2a0c9f2a693dd55e95ca67cff89ae5854 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Thu, 21 Nov 2024 16:31:49 +0000
Subject: [PATCH 08/17] config defaults

---
 apax/cli/templates/md_config_minimal.yaml    |  2 +-
 apax/cli/templates/train_config_full.yaml    | 21 +++++++-------
 apax/cli/templates/train_config_minimal.yaml |  2 +-
 apax/config/lr_config.py                     |  8 +++---
 apax/config/md_config.py                     |  4 +--
 apax/config/model_config.py                  | 29 ++++++++++++--------
 apax/config/train_config.py                  | 23 ++++++++++------
 7 files changed, 50 insertions(+), 39 deletions(-)

diff --git a/apax/cli/templates/md_config_minimal.yaml b/apax/cli/templates/md_config_minimal.yaml
index 4f0a0a4e..d0afd0b7 100644
--- a/apax/cli/templates/md_config_minimal.yaml
+++ b/apax/cli/templates/md_config_minimal.yaml
@@ -13,7 +13,7 @@ ensemble:
 duration: <DURATION> # fs
 n_inner: 100 # compiled innner steps
 sampling_rate: 10  # dump interval
-buffer_size: 100
+buffer_size: 2500
 dr_threshold: 0.5 # Neighborlist skin
 extra_capacity: 0
 
diff --git a/apax/cli/templates/train_config_full.yaml b/apax/cli/templates/train_config_full.yaml
index dadc6d3d..6de499ad 100644
--- a/apax/cli/templates/train_config_full.yaml
+++ b/apax/cli/templates/train_config_full.yaml
@@ -1,7 +1,6 @@
 n_epochs: <NUMBER OF EPOCHS>
 seed: 1
 patience: null
-n_jitted_steps: 1
 data_parallel: True
 weight_average: null
 
@@ -24,7 +23,7 @@ data:
   n_train: 1000
   n_valid: 100
 
-  batch_size: 8
+  batch_size: 4
   valid_batch_size: 100
 
   shift_method: "per_element_regression_shift"
@@ -38,23 +37,23 @@ data:
 
 model:
   name: gmnn
-  use_ntk: false
   basis:
     name: bessel
     n_basis: 16
     r_max: 6.5
 
+  ensemble: null
   # if you would like to use emirical repulsion corrections
   # with the following example.
   # empirical_corrections:
   #   - name: exponential
   #     r_max: 1.5
 
-  ensemble: null
-  # if you would like to train model ensembles, this can be achieved with
-  # the following example.
+  # if you would like to train model ensembles, this can be
+  # achieved with the following example.
+  # Hint: loss type hase to be changed to a probabalistic loss like nll or crps
   # ensemble:
-  #  kind: full
+  #  kind: shallow
   #  n_members: N
 
   n_radial: 5
@@ -69,6 +68,7 @@ model:
   readout_dtype: fp32
   scale_shift_dtype: fp64
   emb_init: uniform
+  use_ntk: false
 
 loss:
 - name: energy
@@ -99,15 +99,14 @@ optimizer:
   zbl_lr: 0.001
   schedule:
     name: cyclic_cosine
-    period: 20
-    decay_factor: 0.90
-
+    period: 40
+    decay_factor: 0.93
 
 callbacks:
 - name: csv
 
 checkpoints:
-  ckpt_interval: 1
+  ckpt_interval: 500
   # The options below are used for transfer learning
   base_model_checkpoint: null
   reset_layers: []
diff --git a/apax/cli/templates/train_config_minimal.yaml b/apax/cli/templates/train_config_minimal.yaml
index 67f96a08..8aaa6d98 100644
--- a/apax/cli/templates/train_config_minimal.yaml
+++ b/apax/cli/templates/train_config_minimal.yaml
@@ -8,7 +8,7 @@ data:
 
   n_train: 1000
   n_valid: 100
-  batch_size: 32
+  batch_size: 4
   valid_batch_size: 100
 
 metrics:
diff --git a/apax/config/lr_config.py b/apax/config/lr_config.py
index 23ccc389..770fdf89 100644
--- a/apax/config/lr_config.py
+++ b/apax/config/lr_config.py
@@ -33,13 +33,13 @@ class CyclicCosineLR(LRSchedule, frozen=True, extra="forbid"):
 
     Parameters
     ----------
-    period: int = 20
+    period: int = 40
         Length of a cycle in epochs.
-    decay_factor: NonNegativeFloat = 1.0
+    decay_factor: NonNegativeFloat = 0.93
         Factor by which to decrease the LR after each cycle.
         1.0 means no decrease.
     """
 
     name: Literal["cyclic_cosine"]
-    period: int = 20
-    decay_factor: NonNegativeFloat = 0.90
+    period: int = 40
+    decay_factor: NonNegativeFloat = 0.93
diff --git a/apax/config/md_config.py b/apax/config/md_config.py
index b93451ce..606809b5 100644
--- a/apax/config/md_config.py
+++ b/apax/config/md_config.py
@@ -234,7 +234,7 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
         | `jax.lax.fori_loop` loop). Also determines atoms buffer size.
     sampling_rate : int, default = 10
         | Interval between saving frames.
-    buffer_size : int, default = 100
+    buffer_size : int, default = 2500
         | Number of collected frames to be dumped at once.
     dr_threshold : float, default = 0.5
         | Skin of the neighborlist.
@@ -275,7 +275,7 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
     duration: PositiveFloat
     n_inner: PositiveInt = 100
     sampling_rate: PositiveInt = 10
-    buffer_size: PositiveInt = 5000
+    buffer_size: PositiveInt = 2500
     dr_threshold: PositiveFloat = 0.5
     extra_capacity: NonNegativeInt = 0
 
diff --git a/apax/config/model_config.py b/apax/config/model_config.py
index 5c4d495d..b7a9ad91 100644
--- a/apax/config/model_config.py
+++ b/apax/config/model_config.py
@@ -35,15 +35,15 @@ class BesselBasisConfig(BaseModel, extra="forbid"):
 
     Parameters
     ----------
-    n_basis : PositiveInt, default = 7
+    n_basis : PositiveInt, default = 16
         Number of uncontracted basis functions.
-    r_max : PositiveFloat, default = 6.0
+    r_max : PositiveFloat, default = 6.5
         Cutoff radius of the descriptor.
     """
 
     name: Literal["bessel"] = "bessel"
     n_basis: PositiveInt = 16
-    r_max: PositiveFloat = 6.0
+    r_max: PositiveFloat = 6.5
 
 
 BasisConfig = Union[GaussianBasisConfig, BesselBasisConfig]
@@ -84,6 +84,11 @@ class ShallowEnsembleConfig(BaseModel, extra="forbid"):
         If set to an integer, the jacobian of ensemble energies wrt. to positions will be computed
         in chunks of that size. This sacrifices some performance for the possibility to use relatively
         large ensemble sizes.
+        
+    Hint
+    ----------
+    Loss type hase to be changed to a probabalistic loss like 'nll' or 'crps'
+    
     """
 
     kind: Literal["shallow"] = "shallow"
@@ -101,12 +106,12 @@ class Correction(BaseModel, extra="forbid"):
 
 class ZBLRepulsion(Correction, extra="forbid"):
     name: Literal["zbl"]
-    r_max: NonNegativeFloat = 2.0
+    r_max: NonNegativeFloat = 1.5
 
 
 class ExponentialRepulsion(Correction, extra="forbid"):
     name: Literal["exponential"]
-    r_max: NonNegativeFloat = 2.0
+    r_max: NonNegativeFloat = 1.5
 
 
 EmpiricalCorrection = Union[ZBLRepulsion, ExponentialRepulsion]
@@ -120,13 +125,13 @@ class BaseModelConfig(BaseModel, extra="forbid"):
     ----------
     basis : BasisConfig, default = GaussianBasisConfig()
         Configuration for primitive basis funtions.
-    nn : List[PositiveInt], default = [512, 512]
+    nn : List[PositiveInt], default = [256, 256]
         Number of hidden layers and units in those layers.
-    w_init : Literal["normal", "lecun"], default = "normal"
+    w_init : Literal["normal", "lecun"], default = "lecun"
         Initialization scheme for the neural network weights.
-    b_init : Literal["normal", "zeros"], default = "normal"
+    b_init : Literal["normal", "zeros"], default = "zeros"
         Initialization scheme for the neural network biases.
-    use_ntk : bool, default = True
+    use_ntk : bool, default = False
         Whether or not to use NTK parametrization.
     ensemble : Optional[EnsembleConfig], default = None
         What kind of model ensemble to use (optional).
@@ -134,17 +139,17 @@ class BaseModelConfig(BaseModel, extra="forbid"):
         Whether to include the ZBL correction.
     calc_stress : bool, default = False
         Whether to calculate stress during model evaluation.
-    descriptor_dtype : Literal["fp32", "fp64"], default = "fp64"
+    descriptor_dtype : Literal["fp32", "fp64"], default = "fp32"
         Data type for descriptor calculations.
     readout_dtype : Literal["fp32", "fp64"], default = "fp32"
         Data type for readout calculations.
-    scale_shift_dtype : Literal["fp32", "fp64"], default = "fp32"
+    scale_shift_dtype : Literal["fp32", "fp64"], default = "fp64"
         Data type for scale and shift parameters.
     """
 
     basis: BasisConfig = Field(BesselBasisConfig(name="bessel"), discriminator="name")
 
-    nn: List[PositiveInt] = [128, 128]
+    nn: List[PositiveInt] = [256, 256]
     w_init: Literal["normal", "lecun"] = "lecun"
     b_init: Literal["normal", "zeros"] = "zeros"
     use_ntk: bool = False
diff --git a/apax/config/train_config.py b/apax/config/train_config.py
index 627d3025..6450b343 100644
--- a/apax/config/train_config.py
+++ b/apax/config/train_config.py
@@ -118,7 +118,10 @@ class DataConfig(BaseModel, extra="forbid"):
         | dict of property name, shape (ragged or fixed) pairs. Currently unused.
     energy_regularisation :
         | Magnitude of the regularization in the per-element energy regression.
-
+    pos_unit : str, default = "Ang"
+        unit of length
+    energy_unit : str, default = "eV"
+        unit of energy
     """
 
     directory: str
@@ -210,16 +213,20 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     ----------
     name : str, default = "adam"
         Name of the optimizer. Can be any `optax` optimizer.
-    emb_lr : NonNegativeFloat, default = 0.02
+    emb_lr : NonNegativeFloat, default = 0.01
         Learning rate of the elemental embedding contraction coefficients.
-    nn_lr : NonNegativeFloat, default = 0.03
+    nn_lr : NonNegativeFloat, default = 0.01
         Learning rate of the neural network parameters.
     scale_lr : NonNegativeFloat, default = 0.001
         Learning rate of the elemental output scaling factors.
-    shift_lr : NonNegativeFloat, default = 0.05
+    shift_lr : NonNegativeFloat, default = 0.03
         Learning rate of the elemental output shifts.
     zbl_lr : NonNegativeFloat, default = 0.001
         Learning rate of the ZBL correction parameters.
+    rep_scale_lr : NonNegativeFloat, default = 0.001
+        LR for the length scale of thes exponential repulsion potential.
+    rep_prefactor_lr : NonNegativeFloat, default = 0.0001
+        LR for the strength of the exponential repulsion potential.
     gradient_clipping: NonNegativeFloat, default = 1000.0
         Per element Gradient clipping value.
         Default is so high that it effectively disabled.
@@ -230,10 +237,10 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     """
 
     name: str = "adam"
-    emb_lr: NonNegativeFloat = 0.02
-    nn_lr: NonNegativeFloat = 0.03
+    emb_lr: NonNegativeFloat = 0.01
+    nn_lr: NonNegativeFloat = 0.01
     scale_lr: NonNegativeFloat = 0.001
-    shift_lr: NonNegativeFloat = 0.05
+    shift_lr: NonNegativeFloat = 0.03
     zbl_lr: NonNegativeFloat = 0.001
     rep_scale_lr: NonNegativeFloat = 0.001
     rep_prefactor_lr: NonNegativeFloat = 0.0001
@@ -362,7 +369,7 @@ class CheckpointConfig(BaseModel, extra="forbid"):
     reset_layers: List of layer names for which the parameters will be reinitialized.
     """
 
-    ckpt_interval: PositiveInt = 1
+    ckpt_interval: PositiveInt = 500
     base_model_checkpoint: Optional[str] = None
     reset_layers: List[str] = []
 

From fb5a7c478d70004583c5cd748908c5e69b29d790 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Thu, 21 Nov 2024 16:31:59 +0000
Subject: [PATCH 09/17] tutorials

---
 examples/01_Model_Training.ipynb       |  56 ++---
 examples/02_Molecular_Dynamics.ipynb   | 130 ++++++------
 examples/03_Transfer_Learning.ipynb    | 136 +++++++------
 examples/04_Batch_Data_Selection.ipynb | 269 +++++++++++--------------
 4 files changed, 292 insertions(+), 299 deletions(-)

diff --git a/examples/01_Model_Training.ipynb b/examples/01_Model_Training.ipynb
index bdcaa16d..8348e2ea 100644
--- a/examples/01_Model_Training.ipynb
+++ b/examples/01_Model_Training.ipynb
@@ -255,23 +255,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732123698.225144  453396 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732123698.228412  453396 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 17:28:20 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 17:28:20 | Initializing Callbacks\n",
-      "INFO | 17:28:20 | Initializing Loss Function\n",
-      "INFO | 17:28:20 | Initializing Metrics\n",
-      "INFO | 17:28:20 | Running Input Pipeline\n",
-      "INFO | 17:28:20 | Reading data file project/ethanol_ccsd_t-train_mod.xyz\n",
-      "INFO | 17:28:20 | Found n_train: 990, n_val: 10\n",
-      "INFO | 17:28:21 | Computing per element energy regression.\n",
-      "INFO | 17:28:21 | Building Standard model\n",
-      "INFO | 17:28:21 | initializing 1 model(s)\n",
-      "INFO | 17:28:28 | Initializing Optimizer\n",
-      "INFO | 17:28:28 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:28:36 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:47<00:00,  2.11it/s, val_loss=0.0237]\n",
-      "INFO | 17:29:15 | Finished training\n"
+      "E0000 00:00:1732191524.593684  470642 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732191524.597022  470642 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:18:48 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 12:18:48 | Initializing Callbacks\n",
+      "INFO | 12:18:48 | Initializing Loss Function\n",
+      "INFO | 12:18:48 | Initializing Metrics\n",
+      "INFO | 12:18:48 | Running Input Pipeline\n",
+      "INFO | 12:18:48 | Reading data file project/ethanol_ccsd_t-train_mod.xyz\n",
+      "INFO | 12:18:48 | Found n_train: 990, n_val: 10\n",
+      "INFO | 12:18:48 | Computing per element energy regression.\n",
+      "INFO | 12:18:49 | Building Standard model\n",
+      "INFO | 12:18:49 | initializing 1 model(s)\n",
+      "INFO | 12:18:55 | Initializing Optimizer\n",
+      "INFO | 12:18:55 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:19:03 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:57<00:00,  1.75it/s, val_loss=0.0931]\n",
+      "INFO | 12:19:52 | Finished training\n"
      ]
     }
    ],
@@ -310,10 +310,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732123756.998387  453263 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732123757.001735  453263 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:29:34 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:51<00:00,  1.93it/s, val_loss=0.0234]\n"
+      "E0000 00:00:1732191594.092194  470507 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732191594.095534  470507 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:20:12 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [01:00<00:00,  1.65it/s, val_loss=0.0823]\n"
      ]
     }
    ],
@@ -341,7 +341,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -399,7 +399,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Structure: 100%|████████████████████████████████| 999/999 [00:04<00:00, 234.76it/s, test_loss=0.018]\n"
+      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 242.67it/s, test_loss=0.0139]\n"
      ]
     }
    ],
@@ -419,9 +419,9 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732123824.572919  455329 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732123824.576016  455329 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Structure: 100%|███████████████████████████████| 999/999 [00:03<00:00, 253.48it/s, test_loss=0.0218]\n"
+      "E0000 00:00:1732191672.090079  472347 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732191672.093383  472347 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 248.50it/s, test_loss=0.0138]\n"
      ]
     }
    ],
@@ -436,7 +436,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -490,7 +490,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!rm -rf project config_full.yaml eval.log\n"
+    "# !rm -rf project config_full.yaml eval.log\n"
    ]
   },
   {
diff --git a/examples/02_Molecular_Dynamics.ipynb b/examples/02_Molecular_Dynamics.ipynb
index 35d5f1a2..5c0ea519 100644
--- a/examples/02_Molecular_Dynamics.ipynb
+++ b/examples/02_Molecular_Dynamics.ipynb
@@ -36,7 +36,15 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is already a config file in the working directory.\n"
+     ]
+    }
+   ],
    "source": [
     "!apax template train --full  # generating the config file in the cwd"
    ]
@@ -51,10 +59,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732124364.370900  457478 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732124364.374063  457478 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:39:43 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:49<00:00,  2.01it/s, val_loss=0.0176]\n"
+      "E0000 00:00:1732192456.416833  472756 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732192456.419973  472756 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:34:35 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:59<00:00,  1.67it/s, val_loss=0.0435]\n"
      ]
     }
    ],
@@ -185,7 +193,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -326,80 +334,80 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732124446.453100  458649 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732124446.456299  458649 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 17:40:48 | reading structure\n",
-      "INFO | 17:40:48 | Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
-      "INFO | 17:40:48 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
-      "INFO | 17:40:49 | initializing model\n",
-      "INFO | 17:40:49 | loading checkpoint from /SCR1/segreto/dev/apax/examples/project/models/etoh_md/best\n",
-      "INFO | 17:40:49 | Building Standard model\n",
-      "INFO | 17:40:49 | initializing simulation\n",
-      "INFO | 17:40:57 | running simulation for 5.0 ps\n",
-      "Simulation:   5%|█▊                                 | 500/10000 [00:02<00:54, 174.57it/s, T=151.1 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 94679.55it/s]\u001b[A\n",
+      "E0000 00:00:1732192548.207012  473799 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732192548.210026  473799 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:35:51 | reading structure\n",
+      "INFO | 12:35:51 | Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
+      "INFO | 12:35:51 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
+      "INFO | 12:35:51 | initializing model\n",
+      "INFO | 12:35:52 | loading checkpoint from /SCR1/segreto/dev/apax/examples/project/models/etoh_md/best\n",
+      "INFO | 12:35:52 | Building Standard model\n",
+      "INFO | 12:35:52 | initializing simulation\n",
+      "INFO | 12:36:00 | running simulation for 5.0 ps\n",
+      "Simulation:   5%|█▊                                 | 500/10000 [00:02<00:54, 172.75it/s, T=219.0 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 96442.95it/s]\u001b[A\n",
       "\n",
-      "Creating groups: 100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1375.45it/s]\u001b[A\n",
+      "Creating groups: 100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1377.71it/s]\u001b[A\n",
       "\n",
-      "Creating observables: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1709.87it/s]\u001b[A\n",
-      "Simulation:  15%|█████                             | 1500/10000 [00:04<00:18, 453.30it/s, T=182.4 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106535.53it/s]\u001b[A\n",
+      "Creating observables: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1746.90it/s]\u001b[A\n",
+      "Simulation:  15%|█████                             | 1500/10000 [00:04<00:18, 451.01it/s, T=351.5 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 105676.59it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1104.29it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1103.13it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1354.75it/s]\u001b[A\n",
-      "Simulation:  25%|████████▌                         | 2500/10000 [00:05<00:11, 632.12it/s, T=468.3 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 82727.89it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.04it/s]\u001b[A\n",
+      "Simulation:  25%|████████▌                         | 2500/10000 [00:05<00:11, 630.34it/s, T=187.4 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 82338.12it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1115.86it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1094.89it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1358.70it/s]\u001b[A\n",
-      "Simulation:  35%|███████████▉                      | 3500/10000 [00:06<00:08, 733.60it/s, T=213.5 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 111372.92it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1349.52it/s]\u001b[A\n",
+      "Simulation:  35%|███████████▉                      | 3500/10000 [00:06<00:08, 733.28it/s, T=124.0 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 98550.38it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1113.37it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1105.86it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1379.71it/s]\u001b[A\n",
-      "Simulation:  45%|███████████████▎                  | 4500/10000 [00:07<00:06, 787.25it/s, T=368.6 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 110696.86it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1385.63it/s]\u001b[A\n",
+      "Simulation:  45%|███████████████▎                  | 4500/10000 [00:07<00:06, 787.98it/s, T=265.5 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106131.17it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1108.43it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1112.84it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1381.98it/s]\u001b[A\n",
-      "Simulation:  55%|██████████████████▋               | 5500/10000 [00:08<00:05, 814.36it/s, T=549.4 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 79769.95it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1368.45it/s]\u001b[A\n",
+      "Simulation:  55%|██████████████████▋               | 5500/10000 [00:08<00:05, 817.04it/s, T=345.3 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 76664.30it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1104.00it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1107.90it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1374.28it/s]\u001b[A\n",
-      "Simulation:  65%|██████████████████████            | 6500/10000 [00:10<00:04, 828.10it/s, T=277.2 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 116025.01it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.87it/s]\u001b[A\n",
+      "Simulation:  65%|██████████████████████            | 6500/10000 [00:10<00:04, 831.74it/s, T=187.6 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106861.25it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1113.32it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1095.46it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1369.35it/s]\u001b[A\n",
-      "Simulation:  75%|█████████████████████████▌        | 7500/10000 [00:11<00:02, 834.13it/s, T=303.9 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 110726.08it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1338.32it/s]\u001b[A\n",
+      "Simulation:  75%|█████████████████████████▌        | 7500/10000 [00:11<00:02, 837.36it/s, T=418.3 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 109198.23it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1106.68it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1088.75it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1351.69it/s]\u001b[A\n",
-      "Simulation:  85%|████████████████████████████▉     | 8500/10000 [00:12<00:01, 837.66it/s, T=332.7 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 78208.17it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1358.70it/s]\u001b[A\n",
+      "Simulation:  85%|████████████████████████████▉     | 8500/10000 [00:12<00:01, 839.68it/s, T=249.0 K]\n",
+      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 65423.55it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1110.78it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1094.89it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1381.07it/s]\u001b[A\n",
-      "Simulation:  95%|████████████████████████████████▎ | 9500/10000 [00:13<00:00, 839.11it/s, T=288.9 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 112357.46it/s]\u001b[A\n",
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1337.47it/s]\u001b[A\n",
+      "Simulation:  95%|████████████████████████████████▎ | 9500/10000 [00:13<00:00, 840.71it/s, T=514.8 K]\n",
+      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 107463.59it/s]\u001b[A\n",
       "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1109.02it/s]\u001b[A\n",
+      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1090.79it/s]\u001b[A\n",
       "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1365.78it/s]\u001b[A\n",
-      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:14<00:00, 705.17it/s, T=461.2 K]\n",
-      "WARNING | 17:41:11 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "INFO | 17:41:12 | simulation finished after: 14.24 s\n",
-      "INFO | 17:41:12 | performance summary: 30.34 ns/day, 158.23 mu s/step/atom\n"
+      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1328.15it/s]\u001b[A\n",
+      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:14<00:00, 705.16it/s, T=442.2 K]\n",
+      "WARNING | 12:36:14 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "INFO | 12:36:14 | simulation finished after: 14.23 s\n",
+      "INFO | 12:36:14 | performance summary: 30.35 ns/day, 158.15 mu s/step/atom\n"
      ]
     }
    ],
@@ -422,7 +430,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/examples/03_Transfer_Learning.ipynb b/examples/03_Transfer_Learning.ipynb
index cda9a8cd..9d77aaa1 100644
--- a/examples/03_Transfer_Learning.ipynb
+++ b/examples/03_Transfer_Learning.ipynb
@@ -157,23 +157,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732124930.427330  459463 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732124930.430484  459463 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 17:48:53 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 17:48:53 | Initializing Callbacks\n",
-      "INFO | 17:48:53 | Initializing Loss Function\n",
-      "INFO | 17:48:53 | Initializing Metrics\n",
-      "INFO | 17:48:53 | Running Input Pipeline\n",
-      "INFO | 17:48:53 | Reading data file project/benzene_mod.xyz\n",
-      "INFO | 17:49:00 | Found n_train: 1000, n_val: 200\n",
-      "INFO | 17:49:00 | Computing per element energy regression.\n",
-      "INFO | 17:49:01 | Building Standard model\n",
-      "INFO | 17:49:01 | initializing 1 model(s)\n",
-      "INFO | 17:49:08 | Initializing Optimizer\n",
-      "INFO | 17:49:08 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:49:16 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:52<00:00,  1.89it/s, val_loss=0.0206]\n",
-      "INFO | 17:50:01 | Finished training\n"
+      "E0000 00:00:1732193066.962431  477038 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732193066.965633  477038 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:44:30 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 12:44:30 | Initializing Callbacks\n",
+      "INFO | 12:44:30 | Initializing Loss Function\n",
+      "INFO | 12:44:30 | Initializing Metrics\n",
+      "INFO | 12:44:30 | Running Input Pipeline\n",
+      "INFO | 12:44:30 | Reading data file project/benzene_mod.xyz\n",
+      "INFO | 12:44:36 | Found n_train: 1000, n_val: 200\n",
+      "INFO | 12:44:37 | Computing per element energy regression.\n",
+      "INFO | 12:44:38 | Building Standard model\n",
+      "INFO | 12:44:38 | initializing 1 model(s)\n",
+      "INFO | 12:44:45 | Initializing Optimizer\n",
+      "INFO | 12:44:45 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:44:53 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:38<00:00,  2.57it/s, val_loss=0.0333]\n",
+      "INFO | 12:45:24 | Finished training\n"
      ]
     }
    ],
@@ -229,23 +229,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732125003.227050  460393 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732125003.230272  460393 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 17:50:05 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 17:50:05 | Initializing Callbacks\n",
-      "INFO | 17:50:05 | Initializing Loss Function\n",
-      "INFO | 17:50:05 | Initializing Metrics\n",
-      "INFO | 17:50:05 | Running Input Pipeline\n",
-      "INFO | 17:50:05 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 17:50:05 | Found n_train: 50, n_val: 10\n",
-      "INFO | 17:50:05 | Computing per element energy regression.\n",
-      "INFO | 17:50:06 | Building Standard model\n",
-      "INFO | 17:50:06 | initializing 1 model(s)\n",
-      "INFO | 17:50:13 | Initializing Optimizer\n",
-      "INFO | 17:50:13 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:50:20 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|██████████████████████████████████████| 100/100 [00:29<00:00,  3.39it/s, val_loss=0.12]\n",
-      "INFO | 17:50:42 | Finished training\n"
+      "E0000 00:00:1732193125.954706  477849 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732193125.957881  477849 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:45:27 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 12:45:27 | Initializing Callbacks\n",
+      "INFO | 12:45:27 | Initializing Loss Function\n",
+      "INFO | 12:45:27 | Initializing Metrics\n",
+      "INFO | 12:45:27 | Running Input Pipeline\n",
+      "INFO | 12:45:27 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 12:45:27 | Found n_train: 50, n_val: 10\n",
+      "INFO | 12:45:27 | Computing per element energy regression.\n",
+      "INFO | 12:45:28 | Building Standard model\n",
+      "INFO | 12:45:28 | initializing 1 model(s)\n",
+      "INFO | 12:45:35 | Initializing Optimizer\n",
+      "INFO | 12:45:35 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:45:42 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|██████████████████████████████████████| 100/100 [00:16<00:00,  5.98it/s, val_loss=0.12]\n",
+      "INFO | 12:45:52 | Finished training\n"
      ]
     }
    ],
@@ -316,32 +316,32 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732125044.359099  461322 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732125044.362317  461322 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 17:50:46 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 17:50:46 | Initializing Callbacks\n",
-      "INFO | 17:50:46 | Initializing Loss Function\n",
-      "INFO | 17:50:46 | Initializing Metrics\n",
-      "INFO | 17:50:46 | Running Input Pipeline\n",
-      "INFO | 17:50:46 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 17:50:46 | Found n_train: 50, n_val: 10\n",
-      "INFO | 17:50:46 | Computing per element energy regression.\n",
-      "INFO | 17:50:46 | Building Standard model\n",
-      "INFO | 17:50:46 | initializing 1 model(s)\n",
-      "INFO | 17:50:53 | Initializing Optimizer\n",
-      "INFO | 17:50:53 | loading checkpoint from project/models/benzene_dft/best\n",
-      "INFO | 17:50:53 | Transferring parameters from project/models/benzene_dft\n",
-      "INFO | 17:50:53 | Transferring parameter: radial_fn\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_0\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_0\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_1\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_1\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_2\n",
-      "INFO | 17:50:53 | Transferring parameter: dense_2\n",
-      "INFO | 17:50:53 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 17:50:59 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:25<00:00,  3.91it/s, val_loss=0.0078]\n",
-      "INFO | 17:51:19 | Finished training\n"
+      "E0000 00:00:1732193153.668213  478673 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732193153.671491  478673 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:45:56 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 12:45:56 | Initializing Callbacks\n",
+      "INFO | 12:45:56 | Initializing Loss Function\n",
+      "INFO | 12:45:56 | Initializing Metrics\n",
+      "INFO | 12:45:56 | Running Input Pipeline\n",
+      "INFO | 12:45:56 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 12:45:56 | Found n_train: 50, n_val: 10\n",
+      "INFO | 12:45:56 | Computing per element energy regression.\n",
+      "INFO | 12:45:57 | Building Standard model\n",
+      "INFO | 12:45:57 | initializing 1 model(s)\n",
+      "INFO | 12:46:03 | Initializing Optimizer\n",
+      "INFO | 12:46:03 | loading checkpoint from project/models/benzene_dft/best\n",
+      "INFO | 12:46:03 | Transferring parameters from project/models/benzene_dft\n",
+      "INFO | 12:46:03 | Transferring parameter: radial_fn\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_0\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_0\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_1\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_1\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_2\n",
+      "INFO | 12:46:03 | Transferring parameter: dense_2\n",
+      "INFO | 12:46:03 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:46:10 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:11<00:00,  8.34it/s, val_loss=0.0121]\n",
+      "INFO | 12:46:15 | Finished training\n"
      ]
     }
    ],
@@ -357,6 +357,22 @@
     "\n",
     "How much further can you improve the fine-tuning (or pretraining) setup?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !rm -rf project config_full.yaml config_cc_baseline.yaml config_cc_ft.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/04_Batch_Data_Selection.ipynb b/examples/04_Batch_Data_Selection.ipynb
index 14f3deca..a7c67b08 100644
--- a/examples/04_Batch_Data_Selection.ipynb
+++ b/examples/04_Batch_Data_Selection.ipynb
@@ -24,8 +24,8 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732125763.498333  463862 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732125763.501418  463862 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
+      "E0000 00:00:1732193422.542165  479577 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732193422.545349  479577 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
      ]
     }
    ],
@@ -82,15 +82,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There is already a config file in the working directory.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!apax template train --full"
    ]
@@ -133,56 +125,24 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732125768.132223  463950 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732125768.135407  463950 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 18:02:49 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 18:02:49 | Initializing Callbacks\n",
-      "INFO | 18:02:49 | Initializing Loss Function\n",
-      "INFO | 18:02:49 | Initializing Metrics\n",
-      "INFO | 18:02:49 | Running Input Pipeline\n",
-      "INFO | 18:02:49 | Reading training data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 18:02:49 | Reading validation data file project/benzene_ccsd_t-test_mod.xyz\n",
-      "INFO | 18:02:50 | Found n_train: 1000, n_val: 500\n",
-      "INFO | 18:02:50 | Computing per element energy regression.\n",
-      "INFO | 18:02:51 | Building Standard model\n",
-      "INFO | 18:02:51 | initializing 1 model(s)\n",
-      "INFO | 18:02:57 | Initializing Optimizer\n",
-      "INFO | 18:02:57 | Beginning Training\n",
-      "INFO | 18:02:57 | Loading checkpoint\n",
-      "WARNING | 18:02:57 | The transformations API will eventually be replaced by an upgraded design. The current API will not be removed until this point, but it will no longer be actively worked on.\n",
-      "INFO | 18:02:57 | Successfully restored checkpoint from epoch 199\n",
-      "\u001b[31m╭─\u001b[0m\u001b[31m────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m─────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/cli/\u001b[0m\u001b[1;33mapax_app.py\u001b[0m:\u001b[94m46\u001b[0m in \u001b[92mtrain\u001b[0m                      \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 43 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 44 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mfrom\u001b[0m \u001b[4;96mapax\u001b[0m\u001b[4;96m.\u001b[0m\u001b[4;96mtrain\u001b[0m\u001b[4;96m.\u001b[0m\u001b[4;96mrun\u001b[0m \u001b[94mimport\u001b[0m run                                     \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 45 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 46 \u001b[2m│   \u001b[0mrun(train_config_path, log_level)                                  \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 47 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 48 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 49 \u001b[0m\u001b[1;95m@app\u001b[0m.command()                                                         \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/train/\u001b[0m\u001b[1;33mrun.py\u001b[0m:\u001b[94m206\u001b[0m in \u001b[92mrun\u001b[0m                          \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m203 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94melse\u001b[0m:                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m204 \u001b[0m\u001b[2m│   │   \u001b[0mema_handler = \u001b[94mNone\u001b[0m                                             \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m205 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m206 \u001b[2m│   \u001b[0mfit(                                                               \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m207 \u001b[0m\u001b[2m│   │   \u001b[0mstate,                                                         \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m208 \u001b[0m\u001b[2m│   │   \u001b[0mtrain_ds,                                                      \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m209 \u001b[0m\u001b[2m│   │   \u001b[0mloss_fn,                                                       \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[2;33m/SCR1/segreto/dev/apax/apax/train/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m90\u001b[0m in \u001b[92mfit\u001b[0m                       \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 87 \u001b[0m\u001b[2m│   \u001b[0m                                                                   \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 88 \u001b[0m\u001b[2m│   \u001b[0mstate, start_epoch = load_state(state, latest_dir)                 \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 89 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m start_epoch >= n_epochs:                                        \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 90 \u001b[2m│   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 91 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mn_epochs <= current epoch from checkpoint (\u001b[0m\u001b[33m{\u001b[0mn_epochs\u001b[33m}\u001b[0m\u001b[33m <=\u001b[0m \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 92 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                              \u001b[31m│\u001b[0m\n",
-      "\u001b[31m│\u001b[0m   \u001b[2m 93 \u001b[0m                                                                       \u001b[31m│\u001b[0m\n",
-      "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
-      "\u001b[1;91mValueError: \u001b[0mn_epochs <= current epoch from checkpoint \u001b[1m(\u001b[0m\u001b[1;36m200\u001b[0m <= \u001b[1;36m200\u001b[0m\u001b[1m)\u001b[0m\n"
+      "E0000 00:00:1732193427.067501  479680 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732193427.070561  479680 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 12:50:28 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 12:50:28 | Initializing Callbacks\n",
+      "INFO | 12:50:28 | Initializing Loss Function\n",
+      "INFO | 12:50:28 | Initializing Metrics\n",
+      "INFO | 12:50:28 | Running Input Pipeline\n",
+      "INFO | 12:50:28 | Reading training data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 12:50:28 | Reading validation data file project/benzene_ccsd_t-test_mod.xyz\n",
+      "INFO | 12:50:29 | Found n_train: 1000, n_val: 500\n",
+      "INFO | 12:50:29 | Computing per element energy regression.\n",
+      "INFO | 12:50:30 | Building Standard model\n",
+      "INFO | 12:50:30 | initializing 1 model(s)\n",
+      "INFO | 12:50:36 | Initializing Optimizer\n",
+      "INFO | 12:50:36 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/200 [00:00<?, ?it/s]WARNING | 12:50:45 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|███████████████████████████████████| 200/200 [01:52<00:00,  1.77it/s, val_loss=0.00676]\n",
+      "INFO | 12:52:29 | Finished training\n"
      ]
     }
    ],
@@ -220,65 +180,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy per atom: Epot = -524.783eV  Ekin = 0.035eV (T=273K)  Etot = -524.747eV\n",
-      "Energy per atom: Epot = -524.784eV  Ekin = 0.039eV (T=303K)  Etot = -524.745eV\n",
-      "Energy per atom: Epot = -524.789eV  Ekin = 0.040eV (T=310K)  Etot = -524.749eV\n",
-      "Energy per atom: Epot = -524.786eV  Ekin = 0.037eV (T=285K)  Etot = -524.749eV\n",
-      "Energy per atom: Epot = -524.792eV  Ekin = 0.039eV (T=305K)  Etot = -524.753eV\n",
-      "Energy per atom: Epot = -524.778eV  Ekin = 0.039eV (T=301K)  Etot = -524.739eV\n",
-      "Energy per atom: Epot = -524.780eV  Ekin = 0.053eV (T=413K)  Etot = -524.727eV\n",
-      "Energy per atom: Epot = -524.771eV  Ekin = 0.046eV (T=353K)  Etot = -524.726eV\n",
-      "Energy per atom: Epot = -524.775eV  Ekin = 0.051eV (T=393K)  Etot = -524.724eV\n",
-      "Energy per atom: Epot = -524.776eV  Ekin = 0.049eV (T=382K)  Etot = -524.726eV\n",
-      "Energy per atom: Epot = -524.763eV  Ekin = 0.039eV (T=301K)  Etot = -524.724eV\n",
+      "Energy per atom: Epot = -524.782eV  Ekin = 0.045eV (T=345K)  Etot = -524.738eV\n",
+      "Energy per atom: Epot = -524.786eV  Ekin = 0.038eV (T=298K)  Etot = -524.747eV\n",
+      "Energy per atom: Epot = -524.791eV  Ekin = 0.044eV (T=339K)  Etot = -524.748eV\n",
+      "Energy per atom: Epot = -524.797eV  Ekin = 0.054eV (T=415K)  Etot = -524.743eV\n",
+      "Energy per atom: Epot = -524.783eV  Ekin = 0.038eV (T=297K)  Etot = -524.745eV\n",
+      "Energy per atom: Epot = -524.787eV  Ekin = 0.033eV (T=259K)  Etot = -524.754eV\n",
+      "Energy per atom: Epot = -524.777eV  Ekin = 0.045eV (T=345K)  Etot = -524.732eV\n",
+      "Energy per atom: Epot = -524.785eV  Ekin = 0.055eV (T=429K)  Etot = -524.729eV\n",
+      "Energy per atom: Epot = -524.780eV  Ekin = 0.056eV (T=432K)  Etot = -524.724eV\n",
+      "Energy per atom: Epot = -524.766eV  Ekin = 0.046eV (T=355K)  Etot = -524.720eV\n",
+      "Energy per atom: Epot = -524.772eV  Ekin = 0.041eV (T=317K)  Etot = -524.731eV\n",
       "      Step     Time          Energy          fmax\n",
-      "FIRE:    0 18:03:10    -6297.160709        4.643667\n",
-      "FIRE:    1 18:03:10    -6297.568402        1.215557\n",
-      "FIRE:    2 18:03:10    -6297.416246        3.988612\n",
-      "FIRE:    3 18:03:10    -6297.546911        2.973441\n",
-      "FIRE:    4 18:03:10    -6297.678424        1.334519\n",
-      "FIRE:    5 18:03:10    -6297.695949        1.463297\n",
-      "FIRE:    6 18:03:10    -6297.700549        1.363753\n",
-      "FIRE:    7 18:03:10    -6297.708687        1.175066\n",
-      "FIRE:    8 18:03:10    -6297.718344        0.913409\n",
-      "FIRE:    9 18:03:10    -6297.727659        0.727679\n",
-      "FIRE:   10 18:03:10    -6297.736589        0.619541\n",
-      "FIRE:   11 18:03:10    -6297.742862        0.511749\n",
-      "FIRE:   12 18:03:10    -6297.748436        0.697062\n",
-      "FIRE:   13 18:03:10    -6297.755050        0.857826\n",
-      "FIRE:   14 18:03:10    -6297.763414        0.924485\n",
-      "FIRE:   15 18:03:10    -6297.775700        0.863180\n",
-      "FIRE:   16 18:03:10    -6297.790781        0.655139\n",
-      "FIRE:   17 18:03:10    -6297.805233        0.349130\n",
-      "FIRE:   18 18:03:10    -6297.816560        0.393361\n",
-      "FIRE:   19 18:03:10    -6297.826240        0.583213\n",
-      "FIRE:   20 18:03:10    -6297.836858        0.470288\n",
-      "FIRE:   21 18:03:10    -6297.846426        0.208600\n",
-      "FIRE:   22 18:03:10    -6297.847673        0.452071\n",
-      "FIRE:   23 18:03:10    -6297.849254        0.408002\n",
-      "FIRE:   24 18:03:10    -6297.850233        0.325805\n",
-      "FIRE:   25 18:03:10    -6297.851149        0.224788\n",
-      "FIRE:   26 18:03:10    -6297.852456        0.150222\n",
-      "FIRE:   27 18:03:10    -6297.852044        0.165351\n",
-      "FIRE:   28 18:03:10    -6297.852139        0.215623\n",
-      "FIRE:   29 18:03:10    -6297.851961        0.243710\n",
-      "FIRE:   30 18:03:10    -6297.852954        0.222756\n",
-      "FIRE:   31 18:03:10    -6297.854113        0.148616\n",
-      "FIRE:   32 18:03:10    -6297.855736        0.084148\n",
-      "FIRE:   33 18:03:10    -6297.855055        0.126039\n",
-      "FIRE:   34 18:03:10    -6297.855310        0.187920\n",
-      "FIRE:   35 18:03:10    -6297.856459        0.159655\n",
-      "FIRE:   36 18:03:10    -6297.856218        0.078716\n",
-      "FIRE:   37 18:03:10    -6297.857179        0.153488\n",
-      "FIRE:   38 18:03:10    -6297.857669        0.133507\n",
-      "FIRE:   39 18:03:10    -6297.857858        0.097674\n",
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+      "FIRE:   46 12:52:42    -6297.856566        0.032937\n"
      ]
     },
     {
@@ -365,7 +325,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing features: 100%|██████████████████████████████████████| 1148/1148 [00:02<00:00, 410.32it/s]\n"
+      "Computing features: 100%|██████████████████████████████████████| 1148/1148 [00:02<00:00, 434.57it/s]\n"
      ]
     }
    ],
@@ -394,36 +354,36 @@
     {
      "data": {
       "text/plain": [
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-       "         -2.8476998e-07, -2.1711810e-12,  1.0578943e+01],\n",
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        "        ...,\n",
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-       " array([[-8.0443577e-08, -3.9290704e-08, -5.0462347e-07, ...,\n",
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-       "         -2.9376406e-07, -2.1438441e-12,  1.0578943e+01],\n",
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-       "         -2.5320008e-07, -1.6724249e-12,  1.0578943e+01],\n",
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+       " array([[-1.02811443e-16, -1.15199308e-08, -6.91606624e-12, ...,\n",
+       "         -3.46590080e-16, -1.09975839e-11,  7.53225851e+00],\n",
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+       "         -3.19022069e-16, -1.05446875e-11,  7.53225851e+00],\n",
        "        ...,\n",
-       "        [-8.3775660e-08, -4.6998672e-08, -4.5863644e-07, ...,\n",
-       "         -3.1039335e-07, -2.4709093e-12,  1.0578943e+01],\n",
-       "        [-8.3781032e-08, -4.7010957e-08, -4.5846181e-07, ...,\n",
-       "         -3.1048981e-07, -2.4710767e-12,  1.0578943e+01],\n",
-       "        [-8.3768818e-08, -4.7006104e-08, -4.5847912e-07, ...,\n",
-       "         -3.1042489e-07, -2.4719324e-12,  1.0578943e+01]], dtype=float32))"
+       "        [-1.10570702e-16, -1.02983568e-08, -1.36763127e-12, ...,\n",
+       "         -3.16782990e-16, -9.38918214e-12,  7.53225851e+00],\n",
+       "        [-1.10698180e-16, -1.03127560e-08, -1.36684837e-12, ...,\n",
+       "         -3.17149226e-16, -9.39629798e-12,  7.53225851e+00],\n",
+       "        [-1.10852711e-16, -1.03169393e-08, -1.37234115e-12, ...,\n",
+       "         -3.17548020e-16, -9.40592135e-12,  7.53225851e+00]], dtype=float32))"
       ]
      },
      "execution_count": 11,
@@ -462,7 +422,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7efe235b8e90>"
+       "<matplotlib.legend.Legend at 0x7f837c5cab10>"
       ]
      },
      "execution_count": 13,
@@ -471,7 +431,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -492,6 +452,15 @@
     "ax.legend()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf project config_full.yaml"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 4cea8c0509acef5a5634860605310634560c99ae Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Thu, 21 Nov 2024 18:46:36 +0000
Subject: [PATCH 10/17] fix regression test

---
 tests/regression_tests/apax_config.yaml      | 36 +++++++++++---------
 tests/regression_tests/test_apax_training.py | 15 ++++----
 2 files changed, 28 insertions(+), 23 deletions(-)

diff --git a/tests/regression_tests/apax_config.yaml b/tests/regression_tests/apax_config.yaml
index 9021702c..04f53c6c 100644
--- a/tests/regression_tests/apax_config.yaml
+++ b/tests/regression_tests/apax_config.yaml
@@ -10,7 +10,7 @@ data:
   n_train: 1000
   n_valid: 100
 
-  batch_size: 32
+  batch_size: 4
   valid_batch_size: 100
 
   shift_method: "per_element_regression_shift"
@@ -22,22 +22,22 @@ data:
 model:
   name: gmnn
   basis:
-    name: gaussian
-    n_basis: 7
+    name: bessel
+    n_basis: 16
     r_max: 6.5
-    r_min: 0.5
+
   n_radial: 5
-  nn: [512, 512]
+  nn: [256, 256]
 
   calc_stress: false
   empirical_corrections:
     - name: exponential
       r_max: 2.0
 
-  b_init: normal
+  b_init: zeros
   descriptor_dtype: fp32
   readout_dtype: fp32
-  scale_shift_dtype: fp32
+  scale_shift_dtype: fp64
 
 metrics:
   - name: energy
@@ -54,15 +54,15 @@ metrics:
 
 loss:
   - name: energy
-    atoms_exponent: 2
+    atoms_exponent: 1
     weight: 1.0
   - name: forces
     atoms_exponent: 1
-    weight: 8.0
-  - loss_type: cosine_sim
-    atoms_exponent: 1
-    name: forces
-    weight: 0.1
+    weight: 4.0
+  # - loss_type: cosine_sim
+  #   atoms_exponent: 1
+  #   name: forces
+  #   weight: 0.1
   # - loss_type: structures
   #   name: stress
   #   weight: 1.0
@@ -70,12 +70,14 @@ loss:
 optimizer:
   name: adam
   kwargs: {}
-  emb_lr: 0.02
-  nn_lr: 0.03
+  emb_lr: 0.01
+  nn_lr: 0.01
   scale_lr: 0.001
-  shift_lr: 0.05
+  shift_lr: 0.03
   zbl_lr: 0.001
-
+  schedule:
+    name: linear
+    
 callbacks:
   - name: csv
 
diff --git a/tests/regression_tests/test_apax_training.py b/tests/regression_tests/test_apax_training.py
index 2615f78d..52365e43 100644
--- a/tests/regression_tests/test_apax_training.py
+++ b/tests/regression_tests/test_apax_training.py
@@ -39,13 +39,16 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
     current_metrics = load_csv(working_dir / "test/log.csv")
 
     comparison_metrics = {
-        "val_energy_mae": 0.24696787788040334,
-        "val_forces_mae": 0.09672525137916232,
-        "val_forces_mse": 0.017160819058234304,
-        "val_loss": 0.45499257304743396,
+        "val_energy_mae": 0.07457,
+        "val_forces_mae": 0.04777,
+        "val_forces_mse": 0.00423,
+        "val_loss": 0.05094,
     }
-
+    
+    for key in comparison_metrics.keys():
+        print((np.array(current_metrics[key])[-1]))
+            
     for key in comparison_metrics.keys():
         assert (
-            abs((np.array(current_metrics[key])[-1] / comparison_metrics[key]) - 1) < 1e-3
+            abs(np.array(current_metrics[key])[-1] - comparison_metrics[key]) < 0.015
         )

From 7b4caa9ee068b6df7332fc5e2660e74b9379c300 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Thu, 21 Nov 2024 18:49:45 +0000
Subject: [PATCH 11/17] remove comment

---
 apax/md/simulate.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/apax/md/simulate.py b/apax/md/simulate.py
index d9a23d3d..4f299187 100644
--- a/apax/md/simulate.py
+++ b/apax/md/simulate.py
@@ -255,8 +255,6 @@ def run_sim(
         dynamics_checks,
     )
 
-    # constraints = [FixAtoms(indices=[6, 8])]
-
     apply_constraints = create_constraint_function(
         constraints,
         state,

From 1d34610ca8daccb09159b2f128fa7b3d186e300b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 21 Nov 2024 18:49:56 +0000
Subject: [PATCH 12/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 apax/config/model_config.py                  | 4 ++--
 tests/regression_tests/apax_config.yaml      | 2 +-
 tests/regression_tests/test_apax_training.py | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/apax/config/model_config.py b/apax/config/model_config.py
index b7a9ad91..1877a871 100644
--- a/apax/config/model_config.py
+++ b/apax/config/model_config.py
@@ -84,11 +84,11 @@ class ShallowEnsembleConfig(BaseModel, extra="forbid"):
         If set to an integer, the jacobian of ensemble energies wrt. to positions will be computed
         in chunks of that size. This sacrifices some performance for the possibility to use relatively
         large ensemble sizes.
-        
+
     Hint
     ----------
     Loss type hase to be changed to a probabalistic loss like 'nll' or 'crps'
-    
+
     """
 
     kind: Literal["shallow"] = "shallow"
diff --git a/tests/regression_tests/apax_config.yaml b/tests/regression_tests/apax_config.yaml
index 04f53c6c..71971c1a 100644
--- a/tests/regression_tests/apax_config.yaml
+++ b/tests/regression_tests/apax_config.yaml
@@ -77,7 +77,7 @@ optimizer:
   zbl_lr: 0.001
   schedule:
     name: linear
-    
+
 callbacks:
   - name: csv
 
diff --git a/tests/regression_tests/test_apax_training.py b/tests/regression_tests/test_apax_training.py
index 52365e43..f6c06159 100644
--- a/tests/regression_tests/test_apax_training.py
+++ b/tests/regression_tests/test_apax_training.py
@@ -44,10 +44,10 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
         "val_forces_mse": 0.00423,
         "val_loss": 0.05094,
     }
-    
+
     for key in comparison_metrics.keys():
         print((np.array(current_metrics[key])[-1]))
-            
+
     for key in comparison_metrics.keys():
         assert (
             abs(np.array(current_metrics[key])[-1] - comparison_metrics[key]) < 0.015

From f4f3297ba8789de282ec26e32b8610dfece97175 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 21 Nov 2024 19:10:34 +0000
Subject: [PATCH 13/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 apax/train/callbacks.py                      | 1 +
 examples/02_Molecular_Dynamics.ipynb         | 4 ++--
 tests/regression_tests/test_apax_training.py | 4 +---
 3 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/apax/train/callbacks.py b/apax/train/callbacks.py
index 3acaea5e..a89baa66 100644
--- a/apax/train/callbacks.py
+++ b/apax/train/callbacks.py
@@ -49,6 +49,7 @@ def on_test_batch_end(self, batch, logs=None):
         for cb in self.callbacks:
             cb.on_test_batch_end(batch, logs)
 
+
 def format_str(k):
     return f"{k:.5f}"
 
diff --git a/examples/02_Molecular_Dynamics.ipynb b/examples/02_Molecular_Dynamics.ipynb
index 5c0ea519..75efc7f3 100644
--- a/examples/02_Molecular_Dynamics.ipynb
+++ b/examples/02_Molecular_Dynamics.ipynb
@@ -279,8 +279,8 @@
     "    \"ensemble\": {\n",
     "        \"temperature_schedule\": {\n",
     "            \"T0\": 300,\n",
-    "            'name': \"constant\",\n",
-    "            },\n",
+    "            \"name\": \"constant\",\n",
+    "        },\n",
     "    },\n",
     "}\n",
     "config_dict = mod_config(md_config_path, config_updates)\n",
diff --git a/tests/regression_tests/test_apax_training.py b/tests/regression_tests/test_apax_training.py
index f6c06159..8250eecf 100644
--- a/tests/regression_tests/test_apax_training.py
+++ b/tests/regression_tests/test_apax_training.py
@@ -49,6 +49,4 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
         print((np.array(current_metrics[key])[-1]))
 
     for key in comparison_metrics.keys():
-        assert (
-            abs(np.array(current_metrics[key])[-1] - comparison_metrics[key]) < 0.015
-        )
+        assert abs(np.array(current_metrics[key])[-1] - comparison_metrics[key]) < 0.015

From c7e0786f59d33c43487dc98b79b8f7736b363525 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Fri, 22 Nov 2024 13:26:43 +0000
Subject: [PATCH 14/17] update config

---
 apax/cli/templates/md_config_minimal.yaml |  2 +-
 apax/cli/templates/train_config_full.yaml | 12 ++++++------
 apax/config/md_config.py                  |  4 ++--
 apax/config/model_config.py               |  4 ++--
 apax/config/train_config.py               | 20 ++++++++++----------
 5 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/apax/cli/templates/md_config_minimal.yaml b/apax/cli/templates/md_config_minimal.yaml
index d0afd0b7..4f5bcf6b 100644
--- a/apax/cli/templates/md_config_minimal.yaml
+++ b/apax/cli/templates/md_config_minimal.yaml
@@ -11,7 +11,7 @@ ensemble:
     tau: 100
 
 duration: <DURATION> # fs
-n_inner: 100 # compiled innner steps
+n_inner: 500 # compiled innner steps
 sampling_rate: 10  # dump interval
 buffer_size: 2500
 dr_threshold: 0.5 # Neighborlist skin
diff --git a/apax/cli/templates/train_config_full.yaml b/apax/cli/templates/train_config_full.yaml
index 6de499ad..4aef6020 100644
--- a/apax/cli/templates/train_config_full.yaml
+++ b/apax/cli/templates/train_config_full.yaml
@@ -40,7 +40,7 @@ model:
   basis:
     name: bessel
     n_basis: 16
-    r_max: 6.5
+    r_max: 5.0
 
   ensemble: null
   # if you would like to use emirical repulsion corrections
@@ -92,11 +92,11 @@ metrics:
 optimizer:
   name: adam
   kwargs: {}
-  emb_lr: 0.01
-  nn_lr: 0.01
-  scale_lr: 0.001
-  shift_lr: 0.03
-  zbl_lr: 0.001
+  emb_lr: 0.001
+  nn_lr: 0.001
+  scale_lr: 0.0001
+  shift_lr: 0.003
+  zbl_lr: 0.0001
   schedule:
     name: cyclic_cosine
     period: 40
diff --git a/apax/config/md_config.py b/apax/config/md_config.py
index 606809b5..f61bca79 100644
--- a/apax/config/md_config.py
+++ b/apax/config/md_config.py
@@ -229,7 +229,7 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
         | Time step in fs.
     duration : float, required
         | Total simulation time in fs.
-    n_inner : int, default = 100
+    n_inner : int, default = 500
         | Number of compiled simulation steps (i.e. number of iterations of the
         | `jax.lax.fori_loop` loop). Also determines atoms buffer size.
     sampling_rate : int, default = 10
@@ -273,7 +273,7 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
     )
 
     duration: PositiveFloat
-    n_inner: PositiveInt = 100
+    n_inner: PositiveInt = 500
     sampling_rate: PositiveInt = 10
     buffer_size: PositiveInt = 2500
     dr_threshold: PositiveFloat = 0.5
diff --git a/apax/config/model_config.py b/apax/config/model_config.py
index b7a9ad91..dbfb85b2 100644
--- a/apax/config/model_config.py
+++ b/apax/config/model_config.py
@@ -37,13 +37,13 @@ class BesselBasisConfig(BaseModel, extra="forbid"):
     ----------
     n_basis : PositiveInt, default = 16
         Number of uncontracted basis functions.
-    r_max : PositiveFloat, default = 6.5
+    r_max : PositiveFloat, default = 5.0
         Cutoff radius of the descriptor.
     """
 
     name: Literal["bessel"] = "bessel"
     n_basis: PositiveInt = 16
-    r_max: PositiveFloat = 6.5
+    r_max: PositiveFloat = 5.0
 
 
 BasisConfig = Union[GaussianBasisConfig, BesselBasisConfig]
diff --git a/apax/config/train_config.py b/apax/config/train_config.py
index 6450b343..df6e9f6e 100644
--- a/apax/config/train_config.py
+++ b/apax/config/train_config.py
@@ -213,15 +213,15 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     ----------
     name : str, default = "adam"
         Name of the optimizer. Can be any `optax` optimizer.
-    emb_lr : NonNegativeFloat, default = 0.01
+    emb_lr : NonNegativeFloat, default = 0.001
         Learning rate of the elemental embedding contraction coefficients.
-    nn_lr : NonNegativeFloat, default = 0.01
+    nn_lr : NonNegativeFloat, default = 0.001
         Learning rate of the neural network parameters.
-    scale_lr : NonNegativeFloat, default = 0.001
+    scale_lr : NonNegativeFloat, default = 0.0001
         Learning rate of the elemental output scaling factors.
-    shift_lr : NonNegativeFloat, default = 0.03
+    shift_lr : NonNegativeFloat, default = 0.003
         Learning rate of the elemental output shifts.
-    zbl_lr : NonNegativeFloat, default = 0.001
+    zbl_lr : NonNegativeFloat, default = 0.0001
         Learning rate of the ZBL correction parameters.
     rep_scale_lr : NonNegativeFloat, default = 0.001
         LR for the length scale of thes exponential repulsion potential.
@@ -237,11 +237,11 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     """
 
     name: str = "adam"
-    emb_lr: NonNegativeFloat = 0.01
-    nn_lr: NonNegativeFloat = 0.01
-    scale_lr: NonNegativeFloat = 0.001
-    shift_lr: NonNegativeFloat = 0.03
-    zbl_lr: NonNegativeFloat = 0.001
+    emb_lr: NonNegativeFloat = 0.001
+    nn_lr: NonNegativeFloat = 0.001
+    scale_lr: NonNegativeFloat = 0.0001
+    shift_lr: NonNegativeFloat = 0.003
+    zbl_lr: NonNegativeFloat = 0.0001
     rep_scale_lr: NonNegativeFloat = 0.001
     rep_prefactor_lr: NonNegativeFloat = 0.0001
 

From fb2fa78408dc371cdb47f4ee7d8097423ec3b483 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Fri, 22 Nov 2024 13:27:16 +0000
Subject: [PATCH 15/17] update tutorial

---
 examples/01_Model_Training.ipynb       |  57 +++----
 examples/02_Molecular_Dynamics.ipynb   | 103 +++---------
 examples/03_Transfer_Learning.ipynb    | 122 +++++++-------
 examples/04_Batch_Data_Selection.ipynb | 213 ++++++++++---------------
 4 files changed, 194 insertions(+), 301 deletions(-)

diff --git a/examples/01_Model_Training.ipynb b/examples/01_Model_Training.ipynb
index 8348e2ea..5b4bc7e1 100644
--- a/examples/01_Model_Training.ipynb
+++ b/examples/01_Model_Training.ipynb
@@ -155,13 +155,14 @@
     "\n",
     "```yaml\n",
     "data:\n",
-    "  batch_size: 32\n",
+    "  batch_size: 4\n",
     "  data_path: project/ethanol_ccsd_t-train_mod.xyz\n",
     "  directory: project/models\n",
     "  energy_unit: kcal/mol\n",
     "  experiment: ethanol_ccsd_t_cli\n",
     "  n_train: 990\n",
     "  n_valid: 10\n",
+    "  energy_unit: kcal/mol\n",
     "  pos_unit: Ang\n",
     "  valid_batch_size: 100\n",
     "loss:\n",
@@ -255,23 +256,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732191524.593684  470642 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732191524.597022  470642 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:18:48 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 12:18:48 | Initializing Callbacks\n",
-      "INFO | 12:18:48 | Initializing Loss Function\n",
-      "INFO | 12:18:48 | Initializing Metrics\n",
-      "INFO | 12:18:48 | Running Input Pipeline\n",
-      "INFO | 12:18:48 | Reading data file project/ethanol_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:18:48 | Found n_train: 990, n_val: 10\n",
-      "INFO | 12:18:48 | Computing per element energy regression.\n",
-      "INFO | 12:18:49 | Building Standard model\n",
-      "INFO | 12:18:49 | initializing 1 model(s)\n",
-      "INFO | 12:18:55 | Initializing Optimizer\n",
-      "INFO | 12:18:55 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:19:03 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:57<00:00,  1.75it/s, val_loss=0.0931]\n",
-      "INFO | 12:19:52 | Finished training\n"
+      "E0000 00:00:1732268187.845256  520474 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268187.848463  520474 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:36:31 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 09:36:31 | Initializing Callbacks\n",
+      "INFO | 09:36:32 | Initializing Loss Function\n",
+      "INFO | 09:36:32 | Initializing Metrics\n",
+      "INFO | 09:36:32 | Running Input Pipeline\n",
+      "INFO | 09:36:32 | Reading data file project/ethanol_ccsd_t-train_mod.xyz\n",
+      "INFO | 09:36:32 | Found n_train: 990, n_val: 10\n",
+      "INFO | 09:36:32 | Computing per element energy regression.\n",
+      "INFO | 09:36:33 | Building Standard model\n",
+      "INFO | 09:36:33 | initializing 1 model(s)\n",
+      "INFO | 09:36:40 | Initializing Optimizer\n",
+      "INFO | 09:36:40 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:36:47 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:59<00:00,  1.69it/s, val_loss=0.0287]\n",
+      "INFO | 09:37:39 | Finished training\n"
      ]
     }
    ],
@@ -310,10 +311,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732191594.092194  470507 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732191594.095534  470507 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:20:12 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [01:00<00:00,  1.65it/s, val_loss=0.0823]\n"
+      "E0000 00:00:1732268260.595895  520335 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268260.599126  520335 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:37:58 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [01:01<00:00,  1.62it/s, val_loss=0.0287]\n"
      ]
     }
    ],
@@ -341,7 +342,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
@@ -399,7 +400,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 242.67it/s, test_loss=0.0139]\n"
+      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 228.47it/s, test_loss=0.0253]\n"
      ]
     }
    ],
@@ -419,9 +420,9 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732191672.090079  472347 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732191672.093383  472347 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 248.50it/s, test_loss=0.0138]\n"
+      "E0000 00:00:1732268339.519757  522195 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268339.522952  522195 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Structure: 100%|███████████████████████████████| 999/999 [00:04<00:00, 229.06it/s, test_loss=0.0253]\n"
      ]
     }
    ],
@@ -436,7 +437,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 4 Axes>"
       ]
diff --git a/examples/02_Molecular_Dynamics.ipynb b/examples/02_Molecular_Dynamics.ipynb
index 5c0ea519..6590871c 100644
--- a/examples/02_Molecular_Dynamics.ipynb
+++ b/examples/02_Molecular_Dynamics.ipynb
@@ -59,10 +59,10 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732192456.416833  472756 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732192456.419973  472756 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:34:35 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:59<00:00,  1.67it/s, val_loss=0.0435]\n"
+      "E0000 00:00:1732268437.776210  522570 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268437.779425  522570 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:40:57 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [01:01<00:00,  1.63it/s, val_loss=0.0287]\n"
      ]
     }
    ],
@@ -193,7 +193,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -334,80 +334,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732192548.207012  473799 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732192548.210026  473799 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:35:51 | reading structure\n",
-      "INFO | 12:35:51 | Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
-      "INFO | 12:35:51 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
-      "INFO | 12:35:51 | initializing model\n",
-      "INFO | 12:35:52 | loading checkpoint from /SCR1/segreto/dev/apax/examples/project/models/etoh_md/best\n",
-      "INFO | 12:35:52 | Building Standard model\n",
-      "INFO | 12:35:52 | initializing simulation\n",
-      "INFO | 12:36:00 | running simulation for 5.0 ps\n",
-      "Simulation:   5%|█▊                                 | 500/10000 [00:02<00:54, 172.75it/s, T=219.0 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 96442.95it/s]\u001b[A\n",
-      "\n",
-      "Creating groups: 100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1377.71it/s]\u001b[A\n",
-      "\n",
-      "Creating observables: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1746.90it/s]\u001b[A\n",
-      "Simulation:  15%|█████                             | 1500/10000 [00:04<00:18, 451.01it/s, T=351.5 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 105676.59it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1103.13it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.04it/s]\u001b[A\n",
-      "Simulation:  25%|████████▌                         | 2500/10000 [00:05<00:11, 630.34it/s, T=187.4 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 82338.12it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1094.89it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1349.52it/s]\u001b[A\n",
-      "Simulation:  35%|███████████▉                      | 3500/10000 [00:06<00:08, 733.28it/s, T=124.0 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 98550.38it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1105.86it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1385.63it/s]\u001b[A\n",
-      "Simulation:  45%|███████████████▎                  | 4500/10000 [00:07<00:06, 787.98it/s, T=265.5 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106131.17it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1112.84it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1368.45it/s]\u001b[A\n",
-      "Simulation:  55%|██████████████████▋               | 5500/10000 [00:08<00:05, 817.04it/s, T=345.3 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 76664.30it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1107.90it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.87it/s]\u001b[A\n",
-      "Simulation:  65%|██████████████████████            | 6500/10000 [00:10<00:04, 831.74it/s, T=187.6 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 106861.25it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1095.46it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1338.32it/s]\u001b[A\n",
-      "Simulation:  75%|█████████████████████████▌        | 7500/10000 [00:11<00:02, 837.36it/s, T=418.3 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 109198.23it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1088.75it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1358.70it/s]\u001b[A\n",
-      "Simulation:  85%|████████████████████████████▉     | 8500/10000 [00:12<00:01, 839.68it/s, T=249.0 K]\n",
-      "Preparing data: 100%|██████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 65423.55it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1094.89it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1337.47it/s]\u001b[A\n",
-      "Simulation:  95%|████████████████████████████████▎ | 9500/10000 [00:13<00:00, 840.71it/s, T=514.8 K]\n",
-      "Preparing data: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 107463.59it/s]\u001b[A\n",
-      "\n",
-      "Extending groups: 100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1090.79it/s]\u001b[A\n",
-      "\n",
-      "Extending observables: 100%|████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1328.15it/s]\u001b[A\n",
-      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:14<00:00, 705.16it/s, T=442.2 K]\n",
-      "WARNING | 12:36:14 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "INFO | 12:36:14 | simulation finished after: 14.23 s\n",
-      "INFO | 12:36:14 | performance summary: 30.35 ns/day, 158.15 mu s/step/atom\n"
+      "E0000 00:00:1732268531.123831  523603 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268531.126852  523603 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:42:13 | reading structure\n",
+      "INFO | 09:42:13 | Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
+      "INFO | 09:42:13 | Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
+      "INFO | 09:42:13 | initializing model\n",
+      "INFO | 09:42:14 | loading checkpoint from /SCR1/segreto/dev/apax/examples/project/models/etoh_md/best\n",
+      "INFO | 09:42:14 | Building Standard model\n",
+      "INFO | 09:42:14 | initializing simulation\n",
+      "INFO | 09:42:22 | running simulation for 5.0 ps\n",
+      "Simulation: 100%|█████████████████████████████████| 10000/10000 [00:14<00:00, 710.91it/s, T=177.5 K]\n",
+      "WARNING | 09:42:36 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Preparing data: 100%|████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 73997.11it/s]\n",
+      "Creating groups: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 256.12it/s]\n",
+      "Creating observables: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1202.15it/s]\n",
+      "INFO | 09:42:36 | simulation finished after: 14.16 s\n",
+      "INFO | 09:42:36 | performance summary: 30.50 ns/day, 157.38 mu s/step/atom\n"
      ]
     }
    ],
@@ -430,7 +373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/examples/03_Transfer_Learning.ipynb b/examples/03_Transfer_Learning.ipynb
index 9d77aaa1..5080e533 100644
--- a/examples/03_Transfer_Learning.ipynb
+++ b/examples/03_Transfer_Learning.ipynb
@@ -157,23 +157,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732193066.962431  477038 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732193066.965633  477038 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:44:30 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 12:44:30 | Initializing Callbacks\n",
-      "INFO | 12:44:30 | Initializing Loss Function\n",
-      "INFO | 12:44:30 | Initializing Metrics\n",
-      "INFO | 12:44:30 | Running Input Pipeline\n",
-      "INFO | 12:44:30 | Reading data file project/benzene_mod.xyz\n",
-      "INFO | 12:44:36 | Found n_train: 1000, n_val: 200\n",
-      "INFO | 12:44:37 | Computing per element energy regression.\n",
-      "INFO | 12:44:38 | Building Standard model\n",
-      "INFO | 12:44:38 | initializing 1 model(s)\n",
-      "INFO | 12:44:45 | Initializing Optimizer\n",
-      "INFO | 12:44:45 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:44:53 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:38<00:00,  2.57it/s, val_loss=0.0333]\n",
-      "INFO | 12:45:24 | Finished training\n"
+      "E0000 00:00:1732268582.750898  524269 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268582.754067  524269 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:43:04 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 09:43:04 | Initializing Callbacks\n",
+      "INFO | 09:43:04 | Initializing Loss Function\n",
+      "INFO | 09:43:04 | Initializing Metrics\n",
+      "INFO | 09:43:04 | Running Input Pipeline\n",
+      "INFO | 09:43:04 | Reading data file project/benzene_mod.xyz\n",
+      "INFO | 09:43:11 | Found n_train: 1000, n_val: 200\n",
+      "INFO | 09:43:11 | Computing per element energy regression.\n",
+      "INFO | 09:43:12 | Building Standard model\n",
+      "INFO | 09:43:12 | initializing 1 model(s)\n",
+      "INFO | 09:43:18 | Initializing Optimizer\n",
+      "INFO | 09:43:18 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:43:27 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:42<00:00,  2.36it/s, val_loss=0.0233]\n",
+      "INFO | 09:44:01 | Finished training\n"
      ]
     }
    ],
@@ -229,23 +229,23 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732193125.954706  477849 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732193125.957881  477849 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:45:27 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 12:45:27 | Initializing Callbacks\n",
-      "INFO | 12:45:27 | Initializing Loss Function\n",
-      "INFO | 12:45:27 | Initializing Metrics\n",
-      "INFO | 12:45:27 | Running Input Pipeline\n",
-      "INFO | 12:45:27 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:45:27 | Found n_train: 50, n_val: 10\n",
-      "INFO | 12:45:27 | Computing per element energy regression.\n",
-      "INFO | 12:45:28 | Building Standard model\n",
-      "INFO | 12:45:28 | initializing 1 model(s)\n",
-      "INFO | 12:45:35 | Initializing Optimizer\n",
-      "INFO | 12:45:35 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:45:42 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|██████████████████████████████████████| 100/100 [00:16<00:00,  5.98it/s, val_loss=0.12]\n",
-      "INFO | 12:45:52 | Finished training\n"
+      "E0000 00:00:1732268643.129714  525084 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268643.132821  525084 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:44:05 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 09:44:05 | Initializing Callbacks\n",
+      "INFO | 09:44:05 | Initializing Loss Function\n",
+      "INFO | 09:44:05 | Initializing Metrics\n",
+      "INFO | 09:44:05 | Running Input Pipeline\n",
+      "INFO | 09:44:05 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 09:44:06 | Found n_train: 50, n_val: 10\n",
+      "INFO | 09:44:06 | Computing per element energy regression.\n",
+      "INFO | 09:44:06 | Building Standard model\n",
+      "INFO | 09:44:06 | initializing 1 model(s)\n",
+      "INFO | 09:44:13 | Initializing Optimizer\n",
+      "INFO | 09:44:13 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:44:20 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|████████████████████████████████████| 100/100 [00:14<00:00,  6.96it/s, val_loss=0.0766]\n",
+      "INFO | 09:44:27 | Finished training\n"
      ]
     }
    ],
@@ -316,32 +316,32 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732193153.668213  478673 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732193153.671491  478673 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:45:56 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 12:45:56 | Initializing Callbacks\n",
-      "INFO | 12:45:56 | Initializing Loss Function\n",
-      "INFO | 12:45:56 | Initializing Metrics\n",
-      "INFO | 12:45:56 | Running Input Pipeline\n",
-      "INFO | 12:45:56 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:45:56 | Found n_train: 50, n_val: 10\n",
-      "INFO | 12:45:56 | Computing per element energy regression.\n",
-      "INFO | 12:45:57 | Building Standard model\n",
-      "INFO | 12:45:57 | initializing 1 model(s)\n",
-      "INFO | 12:46:03 | Initializing Optimizer\n",
-      "INFO | 12:46:03 | loading checkpoint from project/models/benzene_dft/best\n",
-      "INFO | 12:46:03 | Transferring parameters from project/models/benzene_dft\n",
-      "INFO | 12:46:03 | Transferring parameter: radial_fn\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_0\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_0\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_1\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_1\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_2\n",
-      "INFO | 12:46:03 | Transferring parameter: dense_2\n",
-      "INFO | 12:46:03 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 12:46:10 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|████████████████████████████████████| 100/100 [00:11<00:00,  8.34it/s, val_loss=0.0121]\n",
-      "INFO | 12:46:15 | Finished training\n"
+      "E0000 00:00:1732268669.330941  525885 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268669.334048  525885 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:44:31 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 09:44:31 | Initializing Callbacks\n",
+      "INFO | 09:44:31 | Initializing Loss Function\n",
+      "INFO | 09:44:31 | Initializing Metrics\n",
+      "INFO | 09:44:31 | Running Input Pipeline\n",
+      "INFO | 09:44:31 | Reading data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 09:44:31 | Found n_train: 50, n_val: 10\n",
+      "INFO | 09:44:31 | Computing per element energy regression.\n",
+      "INFO | 09:44:31 | Building Standard model\n",
+      "INFO | 09:44:31 | initializing 1 model(s)\n",
+      "INFO | 09:44:38 | Initializing Optimizer\n",
+      "INFO | 09:44:38 | loading checkpoint from project/models/benzene_dft/best\n",
+      "INFO | 09:44:38 | Transferring parameters from project/models/benzene_dft\n",
+      "INFO | 09:44:38 | Transferring parameter: radial_fn\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_0\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_0\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_1\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_1\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_2\n",
+      "INFO | 09:44:38 | Transferring parameter: dense_2\n",
+      "INFO | 09:44:38 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/100 [00:00<?, ?it/s]WARNING | 09:44:44 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|███████████████████████████████████| 100/100 [00:11<00:00,  8.62it/s, val_loss=0.00752]\n",
+      "INFO | 09:44:50 | Finished training\n"
      ]
     }
    ],
@@ -364,7 +364,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !rm -rf project config_full.yaml config_cc_baseline.yaml config_cc_ft.yaml"
+    "!rm -rf project config_full.yaml config_cc_baseline.yaml config_cc_ft.yaml"
    ]
   },
   {
diff --git a/examples/04_Batch_Data_Selection.ipynb b/examples/04_Batch_Data_Selection.ipynb
index a7c67b08..abb58fa1 100644
--- a/examples/04_Batch_Data_Selection.ipynb
+++ b/examples/04_Batch_Data_Selection.ipynb
@@ -24,8 +24,8 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732193422.542165  479577 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732193422.545349  479577 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
+      "E0000 00:00:1732268812.515731  526715 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268812.518770  526715 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
      ]
     }
    ],
@@ -125,24 +125,24 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732193427.067501  479680 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732193427.070561  479680 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "INFO | 12:50:28 | Running on [CudaDevice(id=0)]\n",
-      "INFO | 12:50:28 | Initializing Callbacks\n",
-      "INFO | 12:50:28 | Initializing Loss Function\n",
-      "INFO | 12:50:28 | Initializing Metrics\n",
-      "INFO | 12:50:28 | Running Input Pipeline\n",
-      "INFO | 12:50:28 | Reading training data file project/benzene_ccsd_t-train_mod.xyz\n",
-      "INFO | 12:50:28 | Reading validation data file project/benzene_ccsd_t-test_mod.xyz\n",
-      "INFO | 12:50:29 | Found n_train: 1000, n_val: 500\n",
-      "INFO | 12:50:29 | Computing per element energy regression.\n",
-      "INFO | 12:50:30 | Building Standard model\n",
-      "INFO | 12:50:30 | initializing 1 model(s)\n",
-      "INFO | 12:50:36 | Initializing Optimizer\n",
-      "INFO | 12:50:36 | Beginning Training\n",
-      "Epochs:   0%|                                                               | 0/200 [00:00<?, ?it/s]WARNING | 12:50:45 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
-      "Epochs: 100%|███████████████████████████████████| 200/200 [01:52<00:00,  1.77it/s, val_loss=0.00676]\n",
-      "INFO | 12:52:29 | Finished training\n"
+      "E0000 00:00:1732268817.378390  526800 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1732268817.381573  526800 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "INFO | 09:46:59 | Running on [CudaDevice(id=0)]\n",
+      "INFO | 09:46:59 | Initializing Callbacks\n",
+      "INFO | 09:46:59 | Initializing Loss Function\n",
+      "INFO | 09:46:59 | Initializing Metrics\n",
+      "INFO | 09:46:59 | Running Input Pipeline\n",
+      "INFO | 09:46:59 | Reading training data file project/benzene_ccsd_t-train_mod.xyz\n",
+      "INFO | 09:46:59 | Reading validation data file project/benzene_ccsd_t-test_mod.xyz\n",
+      "INFO | 09:47:00 | Found n_train: 1000, n_val: 500\n",
+      "INFO | 09:47:00 | Computing per element energy regression.\n",
+      "INFO | 09:47:00 | Building Standard model\n",
+      "INFO | 09:47:01 | initializing 1 model(s)\n",
+      "INFO | 09:47:07 | Initializing Optimizer\n",
+      "INFO | 09:47:07 | Beginning Training\n",
+      "Epochs:   0%|                                                               | 0/200 [00:00<?, ?it/s]WARNING | 09:47:16 | SaveArgs.aggregate is deprecated, please use custom TypeHandler (https://orbax.readthedocs.io/en/latest/custom_handlers.html#typehandler) or contact Orbax team to migrate before August 1st, 2024.\n",
+      "Epochs: 100%|███████████████████████████████████| 200/200 [01:56<00:00,  1.72it/s, val_loss=0.00414]\n",
+      "INFO | 09:49:04 | Finished training\n"
      ]
     }
    ],
@@ -180,65 +180,63 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy per atom: Epot = -524.782eV  Ekin = 0.045eV (T=345K)  Etot = -524.738eV\n",
-      "Energy per atom: Epot = -524.786eV  Ekin = 0.038eV (T=298K)  Etot = -524.747eV\n",
-      "Energy per atom: Epot = -524.791eV  Ekin = 0.044eV (T=339K)  Etot = -524.748eV\n",
-      "Energy per atom: Epot = -524.797eV  Ekin = 0.054eV (T=415K)  Etot = -524.743eV\n",
-      "Energy per atom: Epot = -524.783eV  Ekin = 0.038eV (T=297K)  Etot = -524.745eV\n",
-      "Energy per atom: Epot = -524.787eV  Ekin = 0.033eV (T=259K)  Etot = -524.754eV\n",
-      "Energy per atom: Epot = -524.777eV  Ekin = 0.045eV (T=345K)  Etot = -524.732eV\n",
-      "Energy per atom: Epot = -524.785eV  Ekin = 0.055eV (T=429K)  Etot = -524.729eV\n",
-      "Energy per atom: Epot = -524.780eV  Ekin = 0.056eV (T=432K)  Etot = -524.724eV\n",
-      "Energy per atom: Epot = -524.766eV  Ekin = 0.046eV (T=355K)  Etot = -524.720eV\n",
-      "Energy per atom: Epot = -524.772eV  Ekin = 0.041eV (T=317K)  Etot = -524.731eV\n",
+      "Energy per atom: Epot = -524.782eV  Ekin = 0.034eV (T=266K)  Etot = -524.748eV\n",
+      "Energy per atom: Epot = -524.786eV  Ekin = 0.047eV (T=367K)  Etot = -524.739eV\n",
+      "Energy per atom: Epot = -524.789eV  Ekin = 0.044eV (T=344K)  Etot = -524.745eV\n",
+      "Energy per atom: Epot = -524.798eV  Ekin = 0.050eV (T=389K)  Etot = -524.748eV\n",
+      "Energy per atom: Epot = -524.779eV  Ekin = 0.033eV (T=259K)  Etot = -524.745eV\n",
+      "Energy per atom: Epot = -524.778eV  Ekin = 0.029eV (T=226K)  Etot = -524.748eV\n",
+      "Energy per atom: Epot = -524.796eV  Ekin = 0.052eV (T=400K)  Etot = -524.745eV\n",
+      "Energy per atom: Epot = -524.778eV  Ekin = 0.030eV (T=230K)  Etot = -524.748eV\n",
+      "Energy per atom: Epot = -524.783eV  Ekin = 0.031eV (T=242K)  Etot = -524.751eV\n",
+      "Energy per atom: Epot = -524.799eV  Ekin = 0.037eV (T=286K)  Etot = -524.762eV\n",
+      "Energy per atom: Epot = -524.794eV  Ekin = 0.039eV (T=301K)  Etot = -524.755eV\n",
       "      Step     Time          Energy          fmax\n",
-      "FIRE:    0 12:52:42    -6297.264360        5.423857\n",
-      "FIRE:    1 12:52:42    -6297.609658        1.502605\n",
-      "FIRE:    2 12:52:42    -6297.523199        3.777474\n",
-      "FIRE:    3 12:52:42    -6297.613436        2.808250\n",
-      "FIRE:    4 12:52:42    -6297.710647        1.318416\n",
-      "FIRE:    5 12:52:42    -6297.732651        1.002536\n",
-      "FIRE:    6 12:52:42    -6297.734619        0.951556\n",
-      "FIRE:    7 12:52:42    -6297.739271        0.854423\n",
-      "FIRE:    8 12:52:42    -6297.744342        0.720111\n",
-      "FIRE:    9 12:52:42    -6297.748631        0.561626\n",
-      "FIRE:   10 12:52:42    -6297.753220        0.451451\n",
-      "FIRE:   11 12:52:42    -6297.757983        0.410528\n",
-      "FIRE:   12 12:52:42    -6297.760732        0.378981\n",
-      "FIRE:   13 12:52:42    -6297.766621        0.402638\n",
-      "FIRE:   14 12:52:42    -6297.772283        0.429111\n",
-      "FIRE:   15 12:52:42    -6297.777292        0.424979\n",
-      "FIRE:   16 12:52:42    -6297.787293        0.377016\n",
-      "FIRE:   17 12:52:42    -6297.796003        0.292520\n",
-      "FIRE:   18 12:52:42    -6297.803891        0.278990\n",
-      "FIRE:   19 12:52:42    -6297.810827        0.359239\n",
-      "FIRE:   20 12:52:42    -6297.817619        0.371433\n",
-      "FIRE:   21 12:52:42    -6297.826738        0.233386\n",
-      "FIRE:   22 12:52:42    -6297.833571        0.218632\n",
-      "FIRE:   23 12:52:42    -6297.838718        0.295970\n",
-      "FIRE:   24 12:52:42    -6297.843257        0.201275\n",
-      "FIRE:   25 12:52:42    -6297.843875        0.227957\n",
-      "FIRE:   26 12:52:42    -6297.844711        0.182659\n",
-      "FIRE:   27 12:52:42    -6297.845904        0.126734\n",
-      "FIRE:   28 12:52:42    -6297.846748        0.127339\n",
-      "FIRE:   29 12:52:42    -6297.846318        0.127840\n",
-      "FIRE:   30 12:52:42    -6297.847683        0.174191\n",
-      "FIRE:   31 12:52:42    -6297.847996        0.178432\n",
-      "FIRE:   32 12:52:42    -6297.848355        0.137654\n",
-      "FIRE:   33 12:52:42    -6297.849762        0.101414\n",
-      "FIRE:   34 12:52:42    -6297.850318        0.090312\n",
-      "FIRE:   35 12:52:42    -6297.850075        0.117385\n",
-      "FIRE:   36 12:52:42    -6297.851986        0.092180\n",
-      "FIRE:   37 12:52:42    -6297.852179        0.065363\n",
-      "FIRE:   38 12:52:42    -6297.853738        0.110265\n",
-      "FIRE:   39 12:52:42    -6297.853361        0.073035\n",
-      "FIRE:   40 12:52:42    -6297.854324        0.074207\n",
-      "FIRE:   41 12:52:42    -6297.856261        0.099864\n",
-      "FIRE:   42 12:52:42    -6297.856142        0.060285\n",
-      "FIRE:   43 12:52:42    -6297.857665        0.071740\n",
-      "FIRE:   44 12:52:42    -6297.857772        0.112350\n",
-      "FIRE:   45 12:52:42    -6297.856789        0.062306\n",
-      "FIRE:   46 12:52:42    -6297.856566        0.032937\n"
+      "FIRE:    0 09:49:17    -6297.529843        3.433130\n",
+      "FIRE:    1 09:49:17    -6297.707876        0.822871\n",
+      "FIRE:    2 09:49:17    -6297.653997        2.416255\n",
+      "FIRE:    3 09:49:17    -6297.701468        1.822036\n",
+      "FIRE:    4 09:49:17    -6297.755201        0.799443\n",
+      "FIRE:    5 09:49:17    -6297.771731        0.639807\n",
+      "FIRE:    6 09:49:17    -6297.770975        0.635587\n",
+      "FIRE:    7 09:49:17    -6297.774358        0.628206\n",
+      "FIRE:    8 09:49:17    -6297.776426        0.617073\n",
+      "FIRE:    9 09:49:17    -6297.779687        0.603059\n",
+      "FIRE:   10 09:49:17    -6297.782312        0.586707\n",
+      "FIRE:   11 09:49:17    -6297.786402        0.568710\n",
+      "FIRE:   12 09:49:17    -6297.790373        0.550295\n",
+      "FIRE:   13 09:49:17    -6297.793097        0.528365\n",
+      "FIRE:   14 09:49:17    -6297.797588        0.504850\n",
+      "FIRE:   15 09:49:17    -6297.802828        0.477577\n",
+      "FIRE:   16 09:49:17    -6297.810041        0.446206\n",
+      "FIRE:   17 09:49:17    -6297.816479        0.407242\n",
+      "FIRE:   18 09:49:17    -6297.821895        0.358514\n",
+      "FIRE:   19 09:49:17    -6297.828423        0.300577\n",
+      "FIRE:   20 09:49:17    -6297.835409        0.244853\n",
+      "FIRE:   21 09:49:17    -6297.841073        0.207934\n",
+      "FIRE:   22 09:49:17    -6297.844864        0.208605\n",
+      "FIRE:   23 09:49:17    -6297.848110        0.154047\n",
+      "FIRE:   24 09:49:17    -6297.849490        0.151302\n",
+      "FIRE:   25 09:49:17    -6297.848904        0.132234\n",
+      "FIRE:   26 09:49:17    -6297.851377        0.131325\n",
+      "FIRE:   27 09:49:17    -6297.852449        0.132891\n",
+      "FIRE:   28 09:49:17    -6297.851385        0.134429\n",
+      "FIRE:   29 09:49:17    -6297.850981        0.132706\n",
+      "FIRE:   30 09:49:17    -6297.853413        0.127714\n",
+      "FIRE:   31 09:49:17    -6297.853684        0.119362\n",
+      "FIRE:   32 09:49:17    -6297.853303        0.107520\n",
+      "FIRE:   33 09:49:17    -6297.854404        0.095077\n",
+      "FIRE:   34 09:49:17    -6297.853003        0.086662\n",
+      "FIRE:   35 09:49:17    -6297.856393        0.082878\n",
+      "FIRE:   36 09:49:17    -6297.855421        0.077966\n",
+      "FIRE:   37 09:49:17    -6297.855617        0.070408\n",
+      "FIRE:   38 09:49:17    -6297.856790        0.064497\n",
+      "FIRE:   39 09:49:17    -6297.858416        0.063379\n",
+      "FIRE:   40 09:49:17    -6297.857967        0.061494\n",
+      "FIRE:   41 09:49:17    -6297.857515        0.062909\n",
+      "FIRE:   42 09:49:17    -6297.858565        0.051126\n",
+      "FIRE:   43 09:49:17    -6297.859075        0.076019\n",
+      "FIRE:   44 09:49:17    -6297.857729        0.045322\n"
      ]
     },
     {
@@ -304,7 +302,7 @@
     {
      "data": {
       "text/plain": [
-       "148"
+       "146"
       ]
      },
      "execution_count": 9,
@@ -325,7 +323,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing features: 100%|██████████████████████████████████████| 1148/1148 [00:02<00:00, 434.57it/s]\n"
+      "Computing features: 100%|██████████████████████████████████████| 1146/1146 [00:05<00:00, 224.35it/s]\n"
      ]
     }
    ],
@@ -346,55 +344,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([127,  85,  73,  82,  19,  95,  26,  30,   4,   5]),\n",
-       " array([1.2732200e+05, 3.1276562e+02, 9.2281250e+01, 6.8921875e+01,\n",
-       "        2.7906250e+01, 2.3593750e+01, 2.3109375e+01, 1.4125000e+01,\n",
-       "        1.1515625e+01, 1.0906250e+01], dtype=float32),\n",
-       " array([[-1.02811443e-16, -1.15199308e-08, -6.91606624e-12, ...,\n",
-       "         -3.46590080e-16, -1.09975839e-11,  7.53225851e+00],\n",
-       "        [-8.95747069e-17, -1.12410099e-08, -1.78736073e-12, ...,\n",
-       "         -2.70460796e-16, -9.23120261e-12,  7.53225851e+00],\n",
-       "        [-1.09106625e-16, -1.10739000e-08, -1.27026905e-12, ...,\n",
-       "         -3.80384603e-16, -1.05209400e-11,  7.53225851e+00],\n",
-       "        ...,\n",
-       "        [-9.81537147e-17, -1.13610081e-08, -1.53432952e-12, ...,\n",
-       "         -3.13202873e-16, -9.99714547e-12,  7.53225851e+00],\n",
-       "        [-1.01867855e-16, -1.11062928e-08, -2.01783685e-12, ...,\n",
-       "         -3.15307803e-16, -1.00044122e-11,  7.53225851e+00],\n",
-       "        [-1.05319388e-16, -1.19797869e-08, -1.89715279e-12, ...,\n",
-       "         -3.41599361e-16, -1.05473148e-11,  7.53225851e+00]], dtype=float32),\n",
-       " array([[-1.02811443e-16, -1.15199308e-08, -6.91606624e-12, ...,\n",
-       "         -3.46590080e-16, -1.09975839e-11,  7.53225851e+00],\n",
-       "        [-1.46815585e-16, -9.72700320e-09, -3.21329877e-12, ...,\n",
-       "         -5.79306373e-16, -1.21680201e-11,  7.53225851e+00],\n",
-       "        [-1.00780576e-16, -1.24630404e-08, -4.19124934e-12, ...,\n",
-       "         -3.19022069e-16, -1.05446875e-11,  7.53225851e+00],\n",
-       "        ...,\n",
-       "        [-1.10570702e-16, -1.02983568e-08, -1.36763127e-12, ...,\n",
-       "         -3.16782990e-16, -9.38918214e-12,  7.53225851e+00],\n",
-       "        [-1.10698180e-16, -1.03127560e-08, -1.36684837e-12, ...,\n",
-       "         -3.17149226e-16, -9.39629798e-12,  7.53225851e+00],\n",
-       "        [-1.10852711e-16, -1.03169393e-08, -1.37234115e-12, ...,\n",
-       "         -3.17548020e-16, -9.40592135e-12,  7.53225851e+00]], dtype=float32))"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "selected_indices"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -422,7 +371,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f837c5cab10>"
+       "<matplotlib.legend.Legend at 0x7fda74fda790>"
       ]
      },
      "execution_count": 13,
@@ -431,7 +380,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -454,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [

From e0ffcc23ae2824e930aeebac67d3df334ca80b30 Mon Sep 17 00:00:00 2001
From: Tetracarbonylnickel <segreto@theochem.uni-stuttgart.de>
Date: Fri, 22 Nov 2024 13:27:33 +0000
Subject: [PATCH 16/17] test update

---
 tests/regression_tests/test_apax_training.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tests/regression_tests/test_apax_training.py b/tests/regression_tests/test_apax_training.py
index 52365e43..0c39492f 100644
--- a/tests/regression_tests/test_apax_training.py
+++ b/tests/regression_tests/test_apax_training.py
@@ -39,10 +39,10 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
     current_metrics = load_csv(working_dir / "test/log.csv")
 
     comparison_metrics = {
-        "val_energy_mae": 0.07457,
-        "val_forces_mae": 0.04777,
-        "val_forces_mse": 0.00423,
-        "val_loss": 0.05094,
+        "val_energy_mae": 0.075,
+        "val_forces_mae": 0.045,
+        "val_forces_mse": 0.004,
+        "val_loss": 0.045,
     }
     
     for key in comparison_metrics.keys():
@@ -50,5 +50,5 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
             
     for key in comparison_metrics.keys():
         assert (
-            abs(np.array(current_metrics[key])[-1] - comparison_metrics[key]) < 0.015
+            abs((np.array(current_metrics[key])[-1] - comparison_metrics[key])) < 0.01
         )

From a6504728919be38975b5c778589b2e5164354464 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 22 Nov 2024 13:30:46 +0000
Subject: [PATCH 17/17] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 tests/regression_tests/test_apax_training.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tests/regression_tests/test_apax_training.py b/tests/regression_tests/test_apax_training.py
index c3b2cf02..895eda9e 100644
--- a/tests/regression_tests/test_apax_training.py
+++ b/tests/regression_tests/test_apax_training.py
@@ -49,6 +49,4 @@ def test_regression_model_training(get_md22_stachyose, get_tmp_path):
         print((np.array(current_metrics[key])[-1]))
 
     for key in comparison_metrics.keys():
-        assert (
-            abs((np.array(current_metrics[key])[-1] - comparison_metrics[key])) < 0.01
-        )
+        assert abs((np.array(current_metrics[key])[-1] - comparison_metrics[key])) < 0.01