diff --git a/.flake8 b/.flake8
deleted file mode 100644
index ffec67f..0000000
--- a/.flake8
+++ /dev/null
@@ -1,4 +0,0 @@
-[flake8]
-ignore = W503, E203, E402, C901, I001
-max-line-length = 90
-exclude = .git,__pycache__,examples,tmp
diff --git a/erbs/__init__.py b/erbs/__init__.py
index 34d1144..365678d 100644
--- a/erbs/__init__.py
+++ b/erbs/__init__.py
@@ -1,13 +1,13 @@
 import os
 import warnings
 
-from erbs.utils import setup_ase
 import jax
 
+from erbs.utils import setup_ase
+
 os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
 jax.config.update("jax_enable_x64", True)
 
 setup_ase()
 
 warnings.filterwarnings(action="ignore", category=FutureWarning, module=r"jax.*scatter")
-
diff --git a/erbs/ase_main/__init__.py b/erbs/ase_main/__init__.py
index 1b8e1db..92d64f1 100644
--- a/erbs/ase_main/__init__.py
+++ b/erbs/ase_main/__init__.py
@@ -2,4 +2,4 @@
 This submodule contains the PLUMED ASE calculator from the main branch of the ASE repository.
 PyPi projects cannot have GitLab/GitHub dependencies, hence the code for this calculator was copied here.
 On the next ASE release, this submodule will be removed.
-"""
\ No newline at end of file
+"""
diff --git a/erbs/ase_main/calculators/plumed.py b/erbs/ase_main/calculators/plumed.py
index 89a72a3..13c501b 100644
--- a/erbs/ase_main/calculators/plumed.py
+++ b/erbs/ase_main/calculators/plumed.py
@@ -1,14 +1,13 @@
+from os.path import exists
+
+import numpy as np
 from ase.calculators.calculator import Calculator, all_changes
 from ase.io.trajectory import Trajectory
-from ase.parallel import broadcast
-from ase.parallel import world
-import numpy as np
-from os.path import exists
-from ase.units import fs, mol, kJ, nm
+from ase.parallel import broadcast, world
+from ase.units import fs, kJ, mol, nm
 
 
-def restart_from_trajectory(prev_traj, *args, prev_steps=None, atoms=None,
-                            **kwargs):
+def restart_from_trajectory(prev_traj, *args, prev_steps=None, atoms=None, **kwargs):
     """This function helps the user to restart a plumed simulation
     from a trajectory file.
 
@@ -44,10 +43,20 @@ def restart_from_trajectory(prev_traj, *args, prev_steps=None, atoms=None,
 
 
 class Plumed(Calculator):
-    implemented_properties = ['energy', 'forces']
-
-    def __init__(self, calc, input, timestep, atoms=None, kT=1., log='',
-                 restart=False, use_charge=False, update_charge=False):
+    implemented_properties = ["energy", "forces"]
+
+    def __init__(
+        self,
+        calc,
+        input,
+        timestep,
+        atoms=None,
+        kT=1.0,
+        log="",
+        restart=False,
+        use_charge=False,
+        update_charge=False,
+    ):
         """
         Plumed calculator is used for simulations of enhanced sampling methods
         with the open-source code PLUMED (plumed.org).
@@ -109,8 +118,10 @@ def __init__(self, calc, input, timestep, atoms=None, kT=1., log='',
         from plumed import Plumed as pl
 
         if atoms is None:
-            raise TypeError('plumed calculator has to be defined with the \
-                             object atoms inside.')
+            raise TypeError(
+                "plumed calculator has to be defined with the \
+                             object atoms inside."
+            )
 
         self.istep = 0
         Calculator.__init__(self, atoms=atoms)
@@ -124,7 +135,7 @@ def __init__(self, calc, input, timestep, atoms=None, kT=1., log='',
             natoms = len(atoms.get_positions())
             self.plumed = pl()
 
-            ''' Units setup
+            """ Units setup
             warning: inputs and outputs of plumed will still be in
             plumed units.
 
@@ -133,14 +144,14 @@ def __init__(self, calc, input, timestep, atoms=None, kT=1., log='',
             nm to Angstrom
             ps to ASE time units
             ASE and plumed - charge unit is in e units
-            ASE and plumed - mass unit is in a.m.u units '''
+            ASE and plumed - mass unit is in a.m.u units """
 
             ps = 1000 * fs
             self.plumed.cmd("setMDEnergyUnits", mol / kJ)
             self.plumed.cmd("setMDLengthUnits", 1 / nm)
             self.plumed.cmd("setMDTimeUnits", 1 / ps)
-            self.plumed.cmd("setMDChargeUnits", 1.)
-            self.plumed.cmd("setMDMassUnits", 1.)
+            self.plumed.cmd("setMDChargeUnits", 1.0)
+            self.plumed.cmd("setMDMassUnits", 1.0)
 
             self.plumed.cmd("setNatoms", natoms)
             self.plumed.cmd("setMDEngine", "ASE")
@@ -154,17 +165,17 @@ def __init__(self, calc, input, timestep, atoms=None, kT=1., log='',
         self.atoms = atoms
 
     def _get_name(self):
-        return f'{self.calc.name}+Plumed'
+        return f"{self.calc.name}+Plumed"
 
-    def calculate(self, atoms=None, properties=['energy', 'forces'],
-                  system_changes=all_changes):
+    def calculate(
+        self, atoms=None, properties=["energy", "forces"], system_changes=all_changes
+    ):
         Calculator.calculate(self, atoms, properties, system_changes)
 
-        comp = self.compute_energy_and_forces(self.atoms.get_positions(),
-                                              self.istep)
+        comp = self.compute_energy_and_forces(self.atoms.get_positions(), self.istep)
         energy, forces = comp
         self.istep += 1
-        self.results['energy'], self. results['forces'] = energy, forces
+        self.results["energy"], self.results["forces"] = energy, forces
 
     def compute_energy_and_forces(self, pos, istep):
         unbiased_energy = self.calc.get_potential_energy(self.atoms)
@@ -183,11 +194,10 @@ def compute_bias(self, pos, istep, unbiased_energy):
         self.plumed.cmd("setStep", istep)
 
         if self.use_charge:
-            if 'charges' in self.calc.implemented_properties and \
-               self.update_charge:
+            if "charges" in self.calc.implemented_properties and self.update_charge:
                 charges = self.calc.get_charges(atoms=self.atoms.copy())
 
-            elif self.atoms.has('initial_charges') and not self.update_charge:
+            elif self.atoms.has("initial_charges") and not self.update_charge:
                 charges = self.atoms.get_initial_charges()
 
             else:
@@ -215,11 +225,11 @@ def compute_bias(self, pos, istep, unbiased_energy):
         return [energy_bias, forces_bias]
 
     def write_plumed_files(self, images):
-        """ This function computes what is required in
+        """This function computes what is required in
         plumed input for some trajectory.
 
         The outputs are saved in the typical files of
-        plumed such as COLVAR, HILLS """
+        plumed such as COLVAR, HILLS"""
         for i, image in enumerate(images):
             pos = image.get_positions()
             self.compute_energy_and_forces(pos, i)
@@ -231,20 +241,20 @@ def read_plumed_files(self, file_name=None):
             read_files[file_name] = np.loadtxt(file_name, unpack=True)
         else:
             for line in self.input:
-                if line.find('FILE') != -1:
-                    ini = line.find('FILE')
-                    end = line.find(' ', ini)
+                if line.find("FILE") != -1:
+                    ini = line.find("FILE")
+                    end = line.find(" ", ini)
                     if end == -1:
-                        file_name = line[ini + 5:]
+                        file_name = line[ini + 5 :]
                     else:
-                        file_name = line[ini + 5:end]
+                        file_name = line[ini + 5 : end]
                     read_files[file_name] = np.loadtxt(file_name, unpack=True)
 
             if len(read_files) == 0:
-                if exists('COLVAR'):
-                    read_files['COLVAR'] = np.loadtxt('COLVAR', unpack=True)
-                if exists('HILLS'):
-                    read_files['HILLS'] = np.loadtxt('HILLS', unpack=True)
+                if exists("COLVAR"):
+                    read_files["COLVAR"] = np.loadtxt("COLVAR", unpack=True)
+                if exists("HILLS"):
+                    read_files["HILLS"] = np.loadtxt("HILLS", unpack=True)
         assert not len(read_files) == 0, "There are not files for reading"
         return read_files
 
@@ -252,4 +262,4 @@ def __enter__(self):
         return self
 
     def __exit__(self, *args):
-        self.plumed.finalize()
\ No newline at end of file
+        self.plumed.finalize()
diff --git a/erbs/bias/__init__.py b/erbs/bias/__init__.py
index 309fe94..ed4af4b 100644
--- a/erbs/bias/__init__.py
+++ b/erbs/bias/__init__.py
@@ -1,5 +1,4 @@
-from erbs.bias.energy_function_factory import (OPESExploreFactory,
-                                               MetaDFactory)
+from erbs.bias.energy_function_factory import MetaDFactory, OPESExploreFactory
 from erbs.bias.potential import ERBS
 
 __all__ = [
diff --git a/erbs/bias/energy_function_factory.py b/erbs/bias/energy_function_factory.py
index 0d20d6b..ee6421e 100644
--- a/erbs/bias/energy_function_factory.py
+++ b/erbs/bias/energy_function_factory.py
@@ -12,9 +12,7 @@ def __init__(self, T=300, dE=1.2, a=0.3, compression_threshold=0.4) -> None:
         self.compression_threshold = compression_threshold
 
     def create(self, cv_fn, dim_reduction_fn):
-
         def energy_fn(positions, Z, neighbor, box, offsets, bias_state):
-
             g_ref = bias_state.g
             norm = bias_state.normalisation
             cov = bias_state.cov
@@ -31,6 +29,7 @@ def energy_fn(positions, Z, neighbor, box, offsets, bias_state):
 
             total_bias = jnp.sum(kde_ij)
             return total_bias
+
         return energy_fn
 
 
@@ -44,11 +43,8 @@ def __init__(self, T=300, dE=1.2, a=0.3, compression_threshold=0.4) -> None:
         self.gamma = self.dE * self.beta
         self.compression_threshold = compression_threshold
 
-
     def create(self, cv_fn, dim_reduction_fn):
-
         def energy_fn(positions, Z, neighbor, box, offsets, bias_state):
-
             g_ref = bias_state.g
             norm = bias_state.normalisation
             cov = bias_state.cov
diff --git a/erbs/bias/kernel.py b/erbs/bias/kernel.py
index b5b8726..6839011 100644
--- a/erbs/bias/kernel.py
+++ b/erbs/bias/kernel.py
@@ -1,22 +1,24 @@
 import jax.numpy as jnp
 import numpy as np
 
+
 def gaussian(g_diff, k, a):
     x = jnp.sum((g_diff) ** 2)
     U = k * jnp.exp(-x / (a * 2.0))
     return U
 
+
 def diag_gaussian(gdiff, k, cov):
     x = jnp.dot(gdiff.T, gdiff / cov)
-    U = k * jnp.exp(-x/2)
+    U = k * jnp.exp(-x / 2)
     return U
 
 
-def chunked_sum_of_kernels(X, k, cov, chunk_size: int|None=None):
+def chunked_sum_of_kernels(X, k, cov, chunk_size: int | None = None):
     if chunk_size is None:
         gdiff = X[:, None, :] - X[None, :, :]
         cov_broadcast = cov[:, None, :]
-        x = np.einsum('ijd,ijd->ij', gdiff, gdiff / cov_broadcast)
+        x = np.einsum("ijd,ijd->ij", gdiff, gdiff / cov_broadcast)
         G_skk = k * np.exp(-0.5 * x)
 
         return np.sum(G_skk)
@@ -40,7 +42,7 @@ def chunked_sum_of_kernels(X, k, cov, chunk_size: int|None=None):
             # TODO CHECK CHUNKING FOR COV for chunksize = 1
 
             gdiff = Xi[:, None, :] - Xj[None, :, :]
-            
+
             x = np.einsum("bcj, bcj -> bc", gdiff, gdiff / cov_chunk)
             G_skk_chunk = k_chunk * np.exp(-x / 2.0)
             G_skk += np.sum(G_skk_chunk)
@@ -49,28 +51,25 @@ def chunked_sum_of_kernels(X, k, cov, chunk_size: int|None=None):
 
 
 def global_mc_normalisation(g_ref, height, cov):
-
     G_skk = chunked_sum_of_kernels(g_ref, height, cov)
     mc_norm = G_skk / g_ref.shape[0]
 
     return mc_norm
 
 
-
 def mc_normalisation(cluster_models, cluster_idxs, g_ref, height, var):
-
     total_n_clusters = 0
-    elements = sorted(list(cluster_models.keys()))
-    mc_norm = np.zeros(np.max(cluster_idxs) + 1) # Z in the paper
+    elements = sorted(cluster_models.keys())
+    mc_norm = np.zeros(np.max(cluster_idxs) + 1)  # Z in the paper
 
     for element in elements:
         current_n_clusters = 0
         for cluster in range(cluster_models[element].n_clusters):
             current_n_clusters += 1
             cluster_with_offset = cluster + total_n_clusters
-            g_filtered = g_ref[cluster_idxs==cluster_with_offset]
-            height_filtered = height[cluster_idxs==cluster_with_offset]
-            var_filtered = var[cluster_idxs==cluster_with_offset]
+            g_filtered = g_ref[cluster_idxs == cluster_with_offset]
+            height_filtered = height[cluster_idxs == cluster_with_offset]
+            var_filtered = var[cluster_idxs == cluster_with_offset]
 
             G_skk = chunked_sum_of_kernels(g_filtered, height_filtered, var_filtered)
 
@@ -80,27 +79,27 @@ def mc_normalisation(cluster_models, cluster_idxs, g_ref, height, var):
     return mc_norm
 
 
-
 def distances(P1, p2):
     dv = P1 - p2[None, :]
     d = np.linalg.norm(dv, axis=1)
     return d
 
+
 def mahalanobis(P1, Var1, p2):
-    arg = (P1 - p2[None, :])**2 / Var1
+    arg = (P1 - p2[None, :]) ** 2 / Var1
     d = np.sqrt(np.sum(arg, axis=1))
     return d
 
+
 def combine_kernels(h1, h2, p1, p2, var1, var2):
     h = h1 + h2
     p = (h1 * p1 + h2 * p2) / h
-    var = (h1 * (var1 + p1**2) + h2 * (var2 + p2**2))/h - p**2
+    var = (h1 * (var1 + p1**2) + h2 * (var2 + p2**2)) / h - p**2
 
     return p, h, var
 
 
 def compress(g, cov, h, thresh=0.8):
-
     gc = np.full(g.shape, 1000.0)
     covc = np.full(g.shape, 0.0)
     hc = np.full((g.shape[0], 1), 0.0)
@@ -118,7 +117,7 @@ def compress(g, cov, h, thresh=0.8):
         h2 = h[ii]
         cov2 = cov[ii]
 
-        dists = mahalanobis(P1,Cov1, p2)
+        dists = mahalanobis(P1, Cov1, p2)
         dmin = np.min(dists)
         idx = np.argmin(dists)
 
@@ -137,7 +136,7 @@ def compress(g, cov, h, thresh=0.8):
             gc[idx] = pnew
             hc[idx] = hnew
             covc[idx] = covnew
-        
+
     gc = gc[:n_compressed]
     covc = covc[:n_compressed]
     hc = hc[:n_compressed]
@@ -145,7 +144,7 @@ def compress(g, cov, h, thresh=0.8):
 
 
 def incremental_compress(gc, covc, hc, gnew, covnew, hnew, thresh=0.8):
-    dists = mahalanobis(gc,covc, gnew)
+    dists = mahalanobis(gc, covc, gnew)
 
     dmin = np.min(dists)
     idx = np.argmin(dists)
@@ -160,10 +159,10 @@ def incremental_compress(gc, covc, hc, gnew, covnew, hnew, thresh=0.8):
         gc[idx] = pnew
         hc[idx] = hnew
         covc[idx] = covnew
-    
+
     else:
-        gc = np.append(gc, gnew[None,:], axis=0)
-        hc = np.append(hc, hnew[None,:], axis=0)
-        covc = np.append(covc, covnew[None,:], axis=0)
-    
+        gc = np.append(gc, gnew[None, :], axis=0)
+        hc = np.append(hc, hnew[None, :], axis=0)
+        covc = np.append(covc, covnew[None, :], axis=0)
+
     return gc, covc, hc
diff --git a/erbs/bias/potential.py b/erbs/bias/potential.py
index 166c3d5..040aba7 100644
--- a/erbs/bias/potential.py
+++ b/erbs/bias/potential.py
@@ -1,17 +1,21 @@
 from functools import partial
 from typing import Optional, Union
+
 import jax
 import jax.numpy as jnp
-from matplotlib.path import Path
 import numpy as np
-from ase.calculators.calculator import Calculator, all_changes
-from apax.utils.jax_md_reduced import partition, space
-from apax.train.checkpoints import restore_parameters
 from apax.config.train_config import Config
-from apax.nn.models import FeatureModel
-from apax.layers.descriptor.basis_functions import RadialFunction, BesselBasis, GaussianBasis
-from apax.layers.descriptor import GaussianMomentDescriptor
 from apax.data.input_pipeline import CachedInMemoryDataset
+from apax.layers.descriptor import GaussianMomentDescriptor
+from apax.layers.descriptor.basis_functions import (
+    GaussianBasis,
+    RadialFunction,
+)
+from apax.nn.models import FeatureModel
+from apax.train.checkpoints import restore_parameters
+from apax.utils.jax_md_reduced import partition, space
+from ase.calculators.calculator import Calculator, all_changes
+from matplotlib.path import Path
 from tqdm import trange
 
 from erbs.bias.energy_function_factory import OPESExploreFactory
@@ -19,7 +23,15 @@
 from erbs.dim_reduction.elementwise_pca import DimReduction
 
 
-def build_feature_neighbor_fns(atoms, n_basis, r_max, dr_threshold, config: Optional[Config]=None, params=None, batched=False):
+def build_feature_neighbor_fns(
+    atoms,
+    n_basis,
+    r_max,
+    dr_threshold,
+    config: Optional[Config] = None,
+    params=None,
+    batched=False,
+):
     box = np.asarray(atoms.get_cell().lengths(), dtype=jnp.float32)
 
     if batched:
@@ -59,10 +71,17 @@ def build_feature_neighbor_fns(atoms, n_basis, r_max, dr_threshold, config: Opti
                     r_min=1.5,
                     r_max=r_max,
                 ),
-                emb_init=None),
-            n_contr=8
+                emb_init=None,
+            ),
+            n_contr=8,
+        )
+        feature_model = FeatureModel(
+            descriptor,
+            readout=None,
+            should_average=True,
+            init_box=box,
+            inference_disp_fn=displacement_fn,
         )
-        feature_model = FeatureModel(descriptor, readout=None, should_average=True, init_box=box, inference_disp_fn=displacement_fn)
         feature_fn = partial(feature_model.apply, {})
     return feature_fn, neighbor_fn
 
@@ -76,12 +95,12 @@ def __init__(
         dim_reduction_factory: DimReduction,
         energy_fn_factory: OPESExploreFactory,
         model_dir: Optional[Union[Path, list[Path]]] = None,
-        n_basis = 5,
+        n_basis=5,
         r_max=6.0,
         dr_threshold=0.5,
         interval=10_000,
         update_iterations=np.inf,
-        **kwargs
+        **kwargs,
     ):
         Calculator.__init__(self, **kwargs)
 
@@ -108,7 +127,7 @@ def __init__(
         self.energy_fn = None
         self.body_fn = None
 
-        self.auxilliary_cvs = [] # used for dimensionality reduction
+        self.auxilliary_cvs = []  # used for dimensionality reduction
         self.ref_cvs = []
         self.bias_state = None
         self.neighbors = None
@@ -120,14 +139,12 @@ def __init__(
 
         self.bias_results = None
 
-
     def _initialize_nl(self, atoms):
         self.cv_fn, self.neighbor_fn = build_feature_neighbor_fns(
             atoms, self.n_basis, self.r_max, self.dr_threshold
         )
         self.cv_fn = jax.jit(self.cv_fn)
 
-
     def update_with_new_dimred(self, g_new):
         self.ref_cvs.append(g_new)
 
@@ -147,7 +164,7 @@ def update_with_new_dimred(self, g_new):
 
         self.bias_state = BiasState(
             std=self.energy_fn_factory.std,
-            g = reduced_ref_cvs,
+            g=reduced_ref_cvs,
             compression_threshold=threshold,
         )
         self.bias_state = self.bias_state.initialize()
@@ -177,7 +194,7 @@ def update_neighbors(self, position, box, is_pbc):
     def update_bias(self, atoms):
         position = jnp.array(atoms.positions, dtype=jnp.float64)
         numbers = jnp.array(atoms.numbers, dtype=jnp.int32)
-        
+
         box = jnp.asarray(atoms.cell.array)
 
         is_pbc = np.any(atoms.get_cell().lengths() > 1e-6)
@@ -187,7 +204,6 @@ def update_bias(self, atoms):
             inv_box = jnp.linalg.inv(box)
             position = space.transform(inv_box, position)
 
-
         self.update_neighbors(position, box, is_pbc)
 
         if self.neighbors.did_buffer_overflow:
@@ -196,7 +212,7 @@ def update_bias(self, atoms):
                 self.neighbors = self.neighbor_fn.allocate(position, box=box)
             else:
                 self.neighbors = self.neighbor_fn.allocate(position)
-        
+
         offsets = jnp.zeros((self.neighbors.idx.shape[1], 3))
         g_new = self.cv_fn(position, numbers, self.neighbors.idx, box, offsets)
 
@@ -204,7 +220,6 @@ def update_bias(self, atoms):
         if self.bias_state is None or should_reinit:
             self.update_with_new_dimred(g_new)
         else:
-            
             self.update_with_fixed_dimred(g_new)
 
         @jax.jit
@@ -220,14 +235,15 @@ def body_fn(positions, neighbor, box, bias_state):
             offsets = jnp.full([neighbor.idx.shape[1], 3], 0)
 
             ef_function = jax.value_and_grad(self.energy_fn)
-            energy, neg_forces = ef_function(positions, numbers, neighbor, box, offsets, bias_state)
+            energy, neg_forces = ef_function(
+                positions, numbers, neighbor, box, offsets, bias_state
+            )
             forces = -neg_forces
             results = {"energy": energy, "forces": forces}
             return results, neighbor
 
         self.body_fn = body_fn
 
-
     def calculate(self, atoms=None, properties=["energy"], system_changes=all_changes):
         Calculator.calculate(self, atoms, properties, system_changes)
 
@@ -259,15 +275,14 @@ def calculate(self, atoms=None, properties=["energy"], system_changes=all_change
             k: np.array(v, dtype=np.float64) for k, v in bias_results.items()
         }
 
-        self.results["energy"] =  self.results["energy"] + self.bias_results["energy"]
-        self.results["forces"] =  self.results["forces"] + self.bias_results["forces"]
+        self.results["energy"] = self.results["energy"] + self.bias_results["energy"]
+        self.results["forces"] = self.results["forces"] + self.bias_results["forces"]
         self.results["energy_bias"] = self.bias_results["energy"]
         self.results["forces_bias"] = self.bias_results["forces"]
 
         self._step_counter += 1
 
     def compute_cvs(self, atoms_list, batch_size=4):
-    
         dataset = CachedInMemoryDataset(
             atoms_list,
             self.r_max,
@@ -279,7 +294,13 @@ def compute_cvs(self, atoms_list, batch_size=4):
         n_data = dataset.n_data
         ds = dataset.batch()
 
-        self.cv_fn, _ = build_feature_neighbor_fns(atoms_list[0], self.n_basis, self.r_max, dr_threshold=self.dr_threshold, batched=True)
+        self.cv_fn, _ = build_feature_neighbor_fns(
+            atoms_list[0],
+            self.n_basis,
+            self.r_max,
+            dr_threshold=self.dr_threshold,
+            batched=True,
+        )
 
         def calc_descriptor(positions, Z, neighbors, box, offsets):
             g = self.cv_fn(positions, Z, neighbors, box, offsets)
@@ -290,9 +311,7 @@ def calc_descriptor(positions, Z, neighbors, box, offsets):
 
         descriptors = []
 
-        pbar = trange(
-            n_data, desc="Evaluating data", ncols=100, leave=False
-        )
+        pbar = trange(n_data, desc="Evaluating data", ncols=100, leave=False)
         for i, inputs in enumerate(ds):
             g = calc_descriptor(
                 inputs["positions"],
@@ -306,7 +325,6 @@ def calc_descriptor(positions, Z, neighbors, box, offsets):
             for j in range(num_strucutres_in_batch):
                 g_cpu = np.asarray(g[j])
                 descriptors.append(g_cpu)
-                
 
             pbar.update(batch_size)
         pbar.close()
@@ -315,7 +333,6 @@ def calc_descriptor(positions, Z, neighbors, box, offsets):
         return descriptors
 
     def add_configs(self, atoms_list, batch_size=4, for_dimred_only=True):
-
         descriptors = self.compute_cvs(atoms_list, batch_size)
 
         if for_dimred_only:
@@ -323,22 +340,18 @@ def add_configs(self, atoms_list, batch_size=4, for_dimred_only=True):
         else:
             self.ref_cvs.extend(descriptors)
 
-
     def add_descriptors(self, path, for_dimred_only=True):
         data = np.load(path)
         descriptors = data["g"]
 
-        descriptors = [entry for entry in descriptors]
+        descriptors = list(descriptors)
         if for_dimred_only:
             self.auxilliary_cvs.extend(descriptors)
         else:
             self.ref_cvs.extend(descriptors)
 
-
     def save_descriptors(self, path):
-        data = {
-            "g": np.array(self.ref_cvs)
-        }
+        data = {"g": np.array(self.ref_cvs)}
         if len(self.auxilliary_cvs) > 0:
             data["g_aux"] = np.array(self.auxilliary_cvs)
         np.savez(path, **data)
diff --git a/erbs/bias/state.py b/erbs/bias/state.py
index b70b4e3..cd5cd43 100644
--- a/erbs/bias/state.py
+++ b/erbs/bias/state.py
@@ -1,12 +1,12 @@
 import dataclasses
 from typing import Optional
+
 import jax.numpy as jnp
 import numpy as np
-from erbs.bias.kernel import compress, global_mc_normalisation, incremental_compress
 from flax.struct import dataclass
-
 from jax import Array
 
+from erbs.bias.kernel import compress, global_mc_normalisation, incremental_compress
 
 
 @dataclass
@@ -21,12 +21,12 @@ class BiasState:
 
     def replace(self, **kwargs) -> "BiasState":
         return dataclasses.replace(self, **kwargs)
-    
+
     def initialize(self):
         cv_dim = self.g.shape[-1]
-        # TODO use determinant here somewhere 
+        # TODO use determinant here somewhere
         # k0 = self.std ** (cv_dim * 2)
-        k0 = 1 / (np.sqrt(2.0 * np.pi * self.std**2) **cv_dim )
+        k0 = 1 / (np.sqrt(2.0 * np.pi * self.std**2) ** cv_dim)
         height = jnp.full((self.g.shape[0], 1), k0)
 
         cov = jnp.full(self.g.shape, self.std)
@@ -38,38 +38,45 @@ def initialize(self):
         )
 
         return self.replace(height=height, cov=cov, normalisation=Zn)
-    
 
     def compress(self):
-
         g, cov, height = compress(
-            self.g,
-            self.cov,
-            self.height,
-            thresh=self.compression_threshold
+            self.g, self.cov, self.height, thresh=self.compression_threshold
         )
 
         return self.replace(g=g, cov=cov, height=height)
 
     def incremental_compress(self, gnew):
         cv_dim = self.g.shape[-1]
-        covnew = self.std ** (cv_dim * 2) # move to bias state
-        hnew = 1 / (np.sqrt(2.0 * np.pi * self.std**2) **cv_dim )
+        covnew = self.std ** (cv_dim * 2)  # move to bias state
+        hnew = 1 / (np.sqrt(2.0 * np.pi * self.std**2) ** cv_dim)
 
-        g, cov, height = incremental_compress(self.g, self.cov, self.height, gnew, covnew, hnew, thresh=self.compression_threshold)
+        g, cov, height = incremental_compress(
+            self.g,
+            self.cov,
+            self.height,
+            gnew,
+            covnew,
+            hnew,
+            thresh=self.compression_threshold,
+        )
 
         return self.replace(g=g, cov=cov, height=height)
-    
-    def add_configuration(self, gnew):
 
+    def add_configuration(self, gnew):
         cv_dim = self.g.shape[-1]
-        covnew = np.full((cv_dim), self.std) # move to bias state
-        hnew = 1 / (np.sqrt(2.0 * np.pi * self.std**2) **cv_dim )
+        covnew = np.full((cv_dim), self.std)  # move to bias state
+        hnew = 1 / (np.sqrt(2.0 * np.pi * self.std**2) ** cv_dim)
         hnew = np.array([hnew])
 
         g, cov, height = incremental_compress(
-            np.array(self.g), np.array(self.cov), np.array(self.height),
-            np.array(gnew), covnew, hnew, thresh=self.compression_threshold
+            np.array(self.g),
+            np.array(self.cov),
+            np.array(self.height),
+            np.array(gnew),
+            covnew,
+            hnew,
+            thresh=self.compression_threshold,
         )
 
         # for Zn: when compressing kernels, just compute the uncompressed contribution
@@ -83,11 +90,7 @@ def add_configuration(self, gnew):
         )
 
         new_state = self.replace(
-            g=jnp.array(g),
-            cov=jnp.array(cov),
-            height=jnp.array(height),
-            normalisation=Zn
+            g=jnp.array(g), cov=jnp.array(cov), height=jnp.array(height), normalisation=Zn
         )
 
         return new_state
-    
diff --git a/erbs/dim_reduction/__init__.py b/erbs/dim_reduction/__init__.py
index 3a06298..06f0e43 100644
--- a/erbs/dim_reduction/__init__.py
+++ b/erbs/dim_reduction/__init__.py
@@ -1,3 +1,3 @@
-from erbs.dim_reduction.elementwise_pca import ElementwisePCA, ElementwiseLocalPCA
+from erbs.dim_reduction.elementwise_pca import ElementwiseLocalPCA, ElementwisePCA
 
 __all__ = ["ElementwisePCA", "ElementwiseLocalPCA"]
diff --git a/erbs/dim_reduction/elementwise_pca.py b/erbs/dim_reduction/elementwise_pca.py
index 912aab0..de54d96 100644
--- a/erbs/dim_reduction/elementwise_pca.py
+++ b/erbs/dim_reduction/elementwise_pca.py
@@ -1,23 +1,19 @@
 import jax.numpy as jnp
 import numpy as np
+from sklearn.cluster import MiniBatchKMeans
 from sklearn.decomposition import PCA
 from sklearn.metrics import silhouette_score
-from sklearn.cluster import MiniBatchKMeans
 
 
 class DimReduction:
     def __init__(self, n_components=2):
         self.n_components = n_components
-    
-    def fit_transform(self, g, Z):
-        ...
 
-    def apply_clustering(self, g, Z):
-        ...
+    def fit_transform(self, g, Z): ...
 
-    def create_dim_reduction_fn(self):
-        ...
+    def apply_clustering(self, g, Z): ...
 
+    def create_dim_reduction_fn(self): ...
 
 
 def global_flexible_pca(g, n_components):
@@ -53,16 +49,16 @@ def fit_transform(self, g):
         self.sigmaT = sigmaT
 
         if self.skip_first_n_components is not None:
-            g_pca = g_pca[:,self.skip_first_n_components:]
+            g_pca = g_pca[:, self.skip_first_n_components :]
 
         return g_pca
-    
+
     def create_dim_reduction_fn(self):
         def dim_reduction_fn(g_i):
             g_reduced_i = (g_i - self.mu) @ self.sigmaT
 
             if self.skip_first_n_components is not None:
-                g_reduced_i = g_reduced_i[:,self.skip_first_n_components:]
+                g_reduced_i = g_reduced_i[:, self.skip_first_n_components :]
             return g_reduced_i
 
         return dim_reduction_fn
@@ -118,25 +114,25 @@ def dim_reduction_fn(g_i, Z_i):
 
 
 def kmeans_silhouette_score(estimator, X):
-    score = silhouette_score(X, estimator.labels_, metric = 'euclidean')
+    score = silhouette_score(X, estimator.labels_, metric="euclidean")
     return score
 
 
 class KMeansFallback:
-
     def __init__(self) -> None:
         self.labels_ = None
         self.n_clusters = 1
 
     def fit(self, X):
         self.labels_ = np.zeros((X.shape[0]), dtype=np.int32)
+
     def predict(self, X):
         return np.zeros(X.shape[0], dtype=np.int32)
 
 
 def flexible_pca(g_z, n_components, labels, cluster_idx, total_n_clusters):
-    g_cluster = g_z[labels==cluster_idx]
-    idxs = labels[labels==cluster_idx] + total_n_clusters
+    g_cluster = g_z[labels == cluster_idx]
+    idxs = labels[labels == cluster_idx] + total_n_clusters
     n_samples = g_cluster.shape[0]
     if n_samples < n_components:
         mean = np.mean(g_cluster, axis=0)
@@ -160,7 +156,6 @@ def flexible_pca(g_z, n_components, labels, cluster_idx, total_n_clusters):
     return g_pca, mu, sigmaT, idxs
 
 
-
 class ElementwiseLocalPCA(DimReduction):
     def __init__(self, n_components=2, kmax=10) -> None:
         self.n_components = n_components
@@ -176,17 +171,17 @@ def __init__(self, n_components=2, kmax=10) -> None:
     def determine_cluster_range(self, element, n_samples):
         current_num_clusters = self.clusters_per_element[element]
         if current_num_clusters is None:
-            kmin=2
+            kmin = 2
             if n_samples <= 3:
                 lower = 3
             else:
-                lower = n_samples-1
+                lower = n_samples - 1
             kmax = min(lower, self.kmax)
         else:
-            kmin = max(2, current_num_clusters-1)
-            kmax = min(n_samples -1, current_num_clusters+1)
+            kmin = max(2, current_num_clusters - 1)
+            kmax = min(n_samples - 1, current_num_clusters + 1)
         return range(kmin, kmax)
-    
+
     def fit_clustering(self, element, X):
         n_samples = X.shape[0]
 
@@ -201,13 +196,17 @@ def fit_clustering(self, element, X):
 
         for k in cluster_range:
             ndata = X.shape[0]
-            kmeans = MiniBatchKMeans(n_clusters = k, n_init="auto", init="k-means++", max_iter=2).fit(X)
+            kmeans = MiniBatchKMeans(
+                n_clusters=k, n_init="auto", init="k-means++", max_iter=2
+            ).fit(X)
             labels = kmeans.labels_
             if ndata > 2000:
                 sample_size = 2000
             else:
                 sample_size = None
-            score = silhouette_score(X, labels, metric = 'euclidean', sample_size=sample_size)
+            score = silhouette_score(
+                X, labels, metric="euclidean", sample_size=sample_size
+            )
             sil.append(score)
             kmean_models.append(kmeans)
         best_model_idx = np.argmax(sil)
@@ -229,7 +228,7 @@ def fit_transform(self, g, Z):
         mus = []
         sigmaTs = []
         total_n_clusters = 0
-        
+
         for element in elements:
             g_z = g[Z == element]
 
@@ -239,11 +238,7 @@ def fit_transform(self, g, Z):
             n_clusters = cluster_model.n_clusters
             for cluster_idx in range(n_clusters):
                 g_pca, mu, sigmaT, idxs = flexible_pca(
-                    g_z,
-                    self.n_components,
-                    labels,
-                    cluster_idx,
-                    total_n_clusters
+                    g_z, self.n_components, labels, cluster_idx, total_n_clusters
                 )
 
                 mus.append(mu)
@@ -258,9 +253,8 @@ def fit_transform(self, g, Z):
         cluster_idxs = np.concatenate(cluster_idxs, axis=0)
         self.mu, self.sigmaT = jnp.array(mus), jnp.array(sigmaTs)
         return new_gs, cluster_idxs
-    
+
     def apply_clustering(self, g, Z):
-        
         g = np.asarray(g)
         Z = np.asarray(Z)
 
@@ -268,8 +262,8 @@ def apply_clustering(self, g, Z):
         for i, z in enumerate(Z):
             if self.cluster_models[z] is None:
                 raise ValueError(f"No clustering found for element {z}")
-            gi = g[i][None,:]
-            cluster_idx = self.cluster_models[z].predict(gi) 
+            gi = g[i][None, :]
+            cluster_idx = self.cluster_models[z].predict(gi)
             cluster_idxs.append(cluster_idx + self.cluster_offset_per_element[z])
 
         cluster_idxs = jnp.array(cluster_idxs, dtype=jnp.int32)
@@ -277,9 +271,8 @@ def apply_clustering(self, g, Z):
         return cluster_idxs
 
     def create_dim_reduction_fn(self):
-
         def dim_reduction_fn(g_i, cluster_idx):
             g_reduced_i = (g_i - self.mu[cluster_idx]) @ self.sigmaT[cluster_idx]
             return g_reduced_i
 
-        return dim_reduction_fn
\ No newline at end of file
+        return dim_reduction_fn
diff --git a/erbs/nodes/bias_calc.py b/erbs/nodes/bias_calc.py
index 8bb51be..877b391 100644
--- a/erbs/nodes/bias_calc.py
+++ b/erbs/nodes/bias_calc.py
@@ -1,32 +1,36 @@
+import typing as t
+
 import ase
 import numpy as np
+import zntrack
+from ase import units
+
 from erbs.bias.energy_function_factory import OPESExploreFactory
 from erbs.bias.potential import ERBS
+
 # from erbs.dim_reduction.elementwise_pca import ElementwiseLocalPCA
 from erbs.dim_reduction.elementwise_pca import GlobalPCA
-import zntrack
-from ase import units
-import ipsuite as ips
-from typing import Optional
 
-class ERBSCalculator(ips.base.IPSNode):
-    _module_ = "erbs.nodes"
+if t.TYPE_CHECKING:
+    from apax.nodes import Apax
+
 
-    model = zntrack.deps()
-    data: Optional[list[ase.Atoms]] = zntrack.deps(None)
+class ERBSCalculator(zntrack.Node):
+    model: "Apax" = zntrack.deps()
+    data: t.Optional[list[ase.Atoms]] = zntrack.deps(None)
     data_for_dimred_only: bool = zntrack.params(True)
 
     n_basis: float = zntrack.params(4)
     r_min: float = zntrack.params(1.1)
-    r_max = zntrack.params(6.0)
+    r_max: float = zntrack.params(6.0)
     n_contr: float = zntrack.params(8)
 
     pca_components: int = zntrack.params(5)
-    skip_first_n_components: Optional[int] = zntrack.params(None)
+    skip_first_n_components: t.Optional[int] = zntrack.params(None)
 
-    barrier_factor = zntrack.params(3)
+    barrier_factor: float = zntrack.params(3)
     band_width: float = zntrack.params(1.5)
-    temperature = zntrack.params(300)
+    temperature: float = zntrack.params(300)
     bias_interval: int = zntrack.params(2000)
     nl_skin: float = zntrack.params(0.5)
     update_iterations: int = zntrack.params(np.inf)
@@ -36,18 +40,22 @@ def run(self):
 
     def get_calculator(self, **kwargs):
         # zpca = ElementwiseLocalPCA(self.pca_components, self.initial_clusters)
-        pca = GlobalPCA(self.pca_components, skip_first_n_components=self.skip_first_n_components)
+        pca = GlobalPCA(
+            self.pca_components, skip_first_n_components=self.skip_first_n_components
+        )
 
-        dE = units.kB*self.temperature * self.barrier_factor
-        energy_fn_factory = OPESExploreFactory(T=self.temperature, dE=dE, a=self.band_width)
+        dE = units.kB * self.temperature * self.barrier_factor
+        energy_fn_factory = OPESExploreFactory(
+            T=self.temperature, dE=dE, a=self.band_width
+        )
 
         base_calc = self.model.get_calculator()
         calc = ERBS(
             base_calc,
             pca,
             energy_fn_factory,
-            n_basis = self.n_basis,
-            r_min =self.r_min,
+            n_basis=self.n_basis,
+            r_min=self.r_min,
             r_max=self.r_max,
             dr_threshold=self.nl_skin,
             interval=self.bias_interval,
diff --git a/erbs/ops.py b/erbs/ops.py
index c42879c..2dba099 100644
--- a/erbs/ops.py
+++ b/erbs/ops.py
@@ -1,35 +1,41 @@
 from typing import Optional
+
 import jax.numpy as jnp
 from jax.ops import segment_sum
 
 
-def segment_mean(data: jnp.ndarray,
-                 segment_ids: jnp.ndarray,
-                 num_segments: Optional[int] = None,
-                 indices_are_sorted: bool = False,
-                 unique_indices: bool = False):
-  """Returns mean for each segment.
-  Adapted from jraph
-  https://github.com/google-deepmind/jraph/blob/51f5990104f7374492f8f3ea1cbc47feb411c69c/jraph/_src/utils.py#L85
+def segment_mean(
+    data: jnp.ndarray,
+    segment_ids: jnp.ndarray,
+    num_segments: Optional[int] = None,
+    indices_are_sorted: bool = False,
+    unique_indices: bool = False,
+):
+    """Returns mean for each segment.
+    Adapted from jraph
+    https://github.com/google-deepmind/jraph/blob/51f5990104f7374492f8f3ea1cbc47feb411c69c/jraph/_src/utils.py#L85
 
-  Args:
-    data: the values which are averaged segment-wise.
-    segment_ids: indices for the segments.
-    num_segments: total number of segments.
-    indices_are_sorted: whether ``segment_ids`` is known to be sorted.
-    unique_indices: whether ``segment_ids`` is known to be free of duplicates.
-  """
-  nominator = segment_sum(
-      data,
-      segment_ids,
-      num_segments,
-      indices_are_sorted=indices_are_sorted,
-      unique_indices=unique_indices)
-  denominator = segment_sum(
-      jnp.ones_like(data),
-      segment_ids,
-      num_segments,
-      indices_are_sorted=indices_are_sorted,
-      unique_indices=unique_indices)
-  return nominator / jnp.maximum(denominator,
-                                 jnp.ones(shape=[], dtype=denominator.dtype))
\ No newline at end of file
+    Args:
+      data: the values which are averaged segment-wise.
+      segment_ids: indices for the segments.
+      num_segments: total number of segments.
+      indices_are_sorted: whether ``segment_ids`` is known to be sorted.
+      unique_indices: whether ``segment_ids`` is known to be free of duplicates.
+    """
+    nominator = segment_sum(
+        data,
+        segment_ids,
+        num_segments,
+        indices_are_sorted=indices_are_sorted,
+        unique_indices=unique_indices,
+    )
+    denominator = segment_sum(
+        jnp.ones_like(data),
+        segment_ids,
+        num_segments,
+        indices_are_sorted=indices_are_sorted,
+        unique_indices=unique_indices,
+    )
+    return nominator / jnp.maximum(
+        denominator, jnp.ones(shape=[], dtype=denominator.dtype)
+    )
diff --git a/erbs/transformations/__init__.py b/erbs/transformations/__init__.py
index 03e1880..bd5a033 100644
--- a/erbs/transformations/__init__.py
+++ b/erbs/transformations/__init__.py
@@ -1,3 +1,3 @@
 from erbs.transformations.mass_repartition import repartition_hydrogen_mass
 
-__all__ = ["repartition_hydrogen_mass"]
\ No newline at end of file
+__all__ = ["repartition_hydrogen_mass"]
diff --git a/erbs/transformations/mass_repartition.py b/erbs/transformations/mass_repartition.py
index 0538037..0acd131 100644
--- a/erbs/transformations/mass_repartition.py
+++ b/erbs/transformations/mass_repartition.py
@@ -1,13 +1,14 @@
 import numpy as np
 from ase.neighborlist import build_neighbor_list
 
+
 def repartition_hydrogen_mass(atoms, h_mass_scale=3.0):
     atoms = atoms.copy()
 
     nl = build_neighbor_list(atoms, self_interaction=False)
     cm = nl.get_connectivity_matrix(sparse=False)
     z = atoms.get_atomic_numbers()
-    H_idxs = (z==1).nonzero()[0]
+    H_idxs = (z == 1).nonzero()[0]
 
     H_connectivity = cm.T[H_idxs]
     num_hs = np.sum(H_connectivity, axis=0)
@@ -19,7 +20,7 @@ def repartition_hydrogen_mass(atoms, h_mass_scale=3.0):
     mask = np.ones(len(atoms), dtype=np.int32)
     mask[H_idxs] = 0
     mask = np.nonzero(mask)[0]
-    m_scaled[mask] -= 1.008 * num_hs[mask] * (h_mass_scale-1)
+    m_scaled[mask] -= 1.008 * num_hs[mask] * (h_mass_scale - 1)
 
     atoms.set_masses(m_scaled)
-    return atoms
\ No newline at end of file
+    return atoms
diff --git a/erbs/utils.py b/erbs/utils.py
index 55fd76f..478aac1 100644
--- a/erbs/utils.py
+++ b/erbs/utils.py
@@ -12,4 +12,4 @@ def setup_ase():
 
     for val in ERBS_PROPERTIES:
         if val not in all_properties:
-            all_properties.append(val)
\ No newline at end of file
+            all_properties.append(val)
diff --git a/main.py b/main.py
index 69e087e..77bee2f 100644
--- a/main.py
+++ b/main.py
@@ -1,24 +1,25 @@
-from erbs.bias import GKernelBias
-from erbs.dim_reduction import ElementwisePCA, ElementwiseLocalPCA
-from erbs.bias import energy_fn_factory, OPESExploreFactory, MetaDCutFactory
-from erbs.transformations import repartition_hydrogen_mass
-from erbs.descriptor import RBFDescriptorFlax
-from apax.layers.descriptor.basis_functions import RadialFunction, GaussianBasis
-from apax.layers.descriptor import GaussianMomentDescriptor
-
-from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary, ZeroRotation
-from ase import units
-from ase.md.langevin import Langevin
-from ase.md.verlet import VelocityVerlet
 from functools import partial
-from ase.io import read, write
+
 import jax.numpy as jnp
 import numpy as np
-from tqdm import trange
+from apax.layers.descriptor import GaussianMomentDescriptor
+from apax.layers.descriptor.basis_functions import GaussianBasis, RadialFunction
+from ase import units
 from ase.calculators.singlepoint import SinglePointCalculator
-
+from ase.io import read, write
+from ase.md.langevin import Langevin
+from ase.md.velocitydistribution import (
+    MaxwellBoltzmannDistribution,
+    Stationary,
+    ZeroRotation,
+)
+from tqdm import trange
 from xtb.ase.calculator import XTB
 
+from erbs.bias import GKernelBias, OPESExploreFactory, energy_fn_factory
+from erbs.dim_reduction import ElementwiseLocalPCA
+from erbs.transformations import repartition_hydrogen_mass
+
 # path = "raw_data/etoh.traj"
 path = "raw_data/ala2.xyz"
 # path = "raw_data/bmim_opt.extxyz"
@@ -38,14 +39,15 @@
             r_min=1.1,
             r_max=r_max,
         ),
-        emb_init=None),
-    n_contr=8
-) # , Z=jnp.asarray(atoms.numbers)
+        emb_init=None,
+    ),
+    n_contr=8,
+)  # , Z=jnp.asarray(atoms.numbers)
 descriptor_fn = partial(descriptor.apply, {}, box=None, offsets=None)
 
-T=300 
-ts=2.0
-friction=0.001
+T = 300
+ts = 2.0
+friction = 0.001
 # write_interval=1
 remaining_steps = 10000
 bias_interval = 2000
@@ -66,13 +68,13 @@
 # zpca = ElementwisePCA(2)
 zpca = ElementwiseLocalPCA(3, 5)
 
-dE = units.kB*T *3#/ len(atoms)
+dE = units.kB * T * 3  # / len(atoms)
 # dE = 0.046 / len(atoms) - units.kB*T
 energy_fn_factory = OPESExploreFactory(T=T, dE=dE, a=1.5)
 # E_max=1.5/ 22
 # energy_fn_factory = MetaDCutFactory(k=0.01, a=0.3, E_max=E_max)
 
-xtb = XTB(method="gfn-ff") # GFN1-xTB gfn-ff
+xtb = XTB(method="gfn-ff")  # GFN1-xTB gfn-ff
 calc = GKernelBias(
     xtb,
     descriptor_fn,
@@ -139,4 +141,4 @@
 # traj = Trajectory(traj_path, 'w', atoms)
 # dyn.attach(traj.write, interval=write_interval)
 # dyn.run(remaining_steps)
-# traj_path = "raw_data/test_ase.traj"
\ No newline at end of file
+# traj_path = "raw_data/test_ase.traj"
diff --git a/nuts.py b/nuts.py
index d84a01a..c05223a 100644
--- a/nuts.py
+++ b/nuts.py
@@ -1,18 +1,21 @@
+import random
 
-from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary, ZeroRotation
+import numpy as np
 from ase import units
-from ase.md.langevin import Langevin
+from ase.calculators.singlepoint import SinglePointCalculator
+
 # from ase.md.npt import NPT
 from ase.io import read, write
-import numpy as np
+from ase.md.langevin import Langevin
+from ase.md.velocitydistribution import (
+    MaxwellBoltzmannDistribution,
+    Stationary,
+    ZeroRotation,
+)
 from tqdm import trange
-from ase.calculators.singlepoint import SinglePointCalculator
-from ase.md.verlet import VelocityVerlet
-
 from xtb.ase.calculator import XTB
-from erbs.transformations import repartition_hydrogen_mass
 
-import random
+from erbs.transformations import repartition_hydrogen_mass
 
 # path = "raw_data/etoh.traj"
 path = "raw_data/ala2.xyz"
@@ -21,22 +24,21 @@
 atoms = repartition_hydrogen_mass(atoms, 2.0)
 # atoms.wrap()
 
-T=300
-ts=2.0
-friction=0.001
-write_interval=1
+T = 300
+ts = 2.0
+friction = 0.001
+write_interval = 1
 remaining_steps = 10_000
 # max_steps_per_iteration = 1_000
 
 traj_file = "test_data/nuts_nvt.extxyz"
 
 
-calc = XTB(method="gfn-ff") # GFN1-xTB gfn-ff
+calc = XTB(method="gfn-ff")  # GFN1-xTB gfn-ff
 
 atoms.calc = calc
 
 
-
 def run_traj(atoms, calc, ts, T, max_steps):
     del atoms.calc
     atoms.calc = calc
@@ -50,7 +52,7 @@ def run_traj(atoms, calc, ts, T, max_steps):
 
     atoms_cache = []
     # conditions = np.zeros(max_steps)
-    for i in trange(1, max_steps+1, leave=False):
+    for i in trange(1, max_steps + 1, leave=False):
         dyn.run(1)
         # atoms.wrap()
 
@@ -65,7 +67,7 @@ def run_traj(atoms, calc, ts, T, max_steps):
 
         if condition < 0.0:
             break
-    
+
     return atoms_cache, i
 
 
@@ -78,4 +80,3 @@ def run_traj(atoms, calc, ts, T, max_steps):
 
     atoms = random.choice(atoms_cache)
     remaining_steps -= successful_steps
-
diff --git a/nuts_bias.py b/nuts_bias.py
index 91fc257..fe93ff9 100644
--- a/nuts_bias.py
+++ b/nuts_bias.py
@@ -1,25 +1,26 @@
+import random
+from functools import partial
 
-from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary, ZeroRotation
+import numpy as np
 from ase import units
-from ase.md.langevin import Langevin
-from ase.md.nvtberendsen import NVTBerendsen
+from ase.calculators.singlepoint import SinglePointCalculator
+
 # from ase.md.npt import NPT
 from ase.io import read, write
-import numpy as np
+from ase.md.nvtberendsen import NVTBerendsen
+from ase.md.velocitydistribution import (
+    MaxwellBoltzmannDistribution,
+    Stationary,
+    ZeroRotation,
+)
 from tqdm import trange
-from ase.calculators.singlepoint import SinglePointCalculator
-from ase.md.verlet import VelocityVerlet
-
 from xtb.ase.calculator import XTB
-from erbs.transformations import repartition_hydrogen_mass
 
+from erbs.bias import GKernelBias, OPESExploreFactory
 from erbs.descriptor import RBFDescriptorFlax
-from erbs.bias import GKernelBias
 from erbs.dim_reduction import ElementwisePCA
-from erbs.bias import MetaDCutFactory, OPESExploreFactory
-from functools import partial
+from erbs.transformations import repartition_hydrogen_mass
 
-import random
 
 def setup_ase():
     """Add uncertainty keys to ASE all properties."""
@@ -29,6 +30,7 @@ def setup_ase():
         if val not in all_properties:
             all_properties.append(val)
 
+
 setup_ase()
 
 # path = "raw_data/etoh.traj"
@@ -38,17 +40,17 @@ def setup_ase():
 atoms = repartition_hydrogen_mass(atoms, 2.0)
 # atoms.wrap()
 
-T=500
-ts=2.0
-friction=0.001
-write_interval=1
+T = 500
+ts = 2.0
+friction = 0.001
+write_interval = 1
 remaining_steps = 10_000
 # max_steps_per_iteration = 1_000
 
 traj_file = "test_data/nuts_bias_opes_500k_4b_3d_a05_rescale_rand_biasedplacement.extxyz"
 
 
-xtb = XTB(method="gfn-ff") # GFN1-xTB gfn-ff
+xtb = XTB(method="gfn-ff")  # GFN1-xTB gfn-ff
 
 
 r_max = 5.0
@@ -59,7 +61,7 @@ def setup_ase():
 # E_max=1.6/ 22
 # energy_fn_factory = MetaDCutFactory(k=0.02, a=0.2, E_max=E_max)
 
-dE = units.kB*T *4#/ len(atoms)
+dE = units.kB * T * 4  # / len(atoms)
 # dE = 0.046 / len(atoms) - units.kB*T
 energy_fn_factory = OPESExploreFactory(T=T, dE=dE, a=0.5)
 
@@ -79,6 +81,7 @@ def setup_ase():
 calc.add_configs([atoms])
 calc.update_bias(atoms)
 
+
 def run_traj(atoms, calc, ts, T, max_steps):
     del atoms.calc
     atoms.calc = calc
@@ -86,16 +89,17 @@ def run_traj(atoms, calc, ts, T, max_steps):
     Stationary(atoms)
     ZeroRotation(atoms)
 
-
     # dyn = Langevin(atoms, ts * units.fs, friction=friction, temperature_K=T)
     # dyn = VelocityVerlet(atoms, ts * units.fs)
-    dyn = NVTBerendsen(atoms, ts * units.fs, temperature_K=T, taut=0.005*1000*units.fs)
+    dyn = NVTBerendsen(
+        atoms, ts * units.fs, temperature_K=T, taut=0.005 * 1000 * units.fs
+    )
 
     theta0 = np.reshape(atoms.get_positions(), (-1,))
 
     atoms_cache = []
     # conditions = np.zeros(max_steps)
-    for i in trange(1, max_steps+1, leave=False):
+    for i in trange(1, max_steps + 1, leave=False):
         dyn.run(1)
 
         # atoms.wrap()
@@ -106,12 +110,14 @@ def run_traj(atoms, calc, ts, T, max_steps):
         condition = np.dot(d_theta, momenta)
 
         labeled_atoms = atoms.copy()
-        labeled_atoms.calc = SinglePointCalculator(labeled_atoms, energy_biased= calc.results["energy"], **calc.base_results)
+        labeled_atoms.calc = SinglePointCalculator(
+            labeled_atoms, energy_biased=calc.results["energy"], **calc.base_results
+        )
         atoms_cache.append(labeled_atoms)
 
         if condition < 0.0:
             break
-    
+
     return atoms_cache, i
 
 
@@ -123,10 +129,9 @@ def run_traj(atoms, calc, ts, T, max_steps):
         write(traj_file, atoms_cache, format="extxyz", append=True)
 
     atoms = random.choice(atoms_cache)
-    energies = [a.calc.results["energy_biased"] for a in atoms_cache] # _biased
+    energies = [a.calc.results["energy_biased"] for a in atoms_cache]  # _biased
     idx_of_min = np.argmin(energies)
     E_min_atoms = atoms_cache[idx_of_min]
     # atoms = E_min_atoms
     calc.update_bias(E_min_atoms)
     remaining_steps -= successful_steps
-
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index c19c507..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,5918 +0,0 @@
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-
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-description = "Basic inter-process locks"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "zc.lockfile-3.0.post1-py3-none-any.whl", hash = "sha256:ddb2d71088c061dc8a5edbaa346b637d742ca1e1564be75cb98e7dcae715de19"},
-    {file = "zc.lockfile-3.0.post1.tar.gz", hash = "sha256:adb2ee6d9e6a2333c91178dcb2c9b96a5744c78edb7712dc784a7d75648e81ec"},
-]
-
-[package.dependencies]
-setuptools = "*"
-
-[package.extras]
-test = ["zope.testing"]
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
-[[package]]
-name = "znflow"
-version = "0.2.2"
-description = "A general purpose framework for building and running computational graphs."
-optional = false
-python-versions = "<4.0,>=3.9"
-files = [
-    {file = "znflow-0.2.2-py3-none-any.whl", hash = "sha256:0fd83ba42235a1448a347aff1cba83d618a693199d592054d6c540ac18546c28"},
-    {file = "znflow-0.2.2.tar.gz", hash = "sha256:04cea6d52983824888ca6b441aabd686dd88900be69dcb372045e6ae401ac21b"},
-]
-
-[package.dependencies]
-networkx = ">=3,<4"
-
-[package.extras]
-dask = ["dask (>=2024,<2025)", "distributed (>=2024,<2025)"]
-
-[[package]]
-name = "znh5md"
-version = "0.3.5"
-description = "ASE Interface for the H5MD format."
-optional = false
-python-versions = "<4.0,>=3.10"
-files = [
-    {file = "znh5md-0.3.5-py3-none-any.whl", hash = "sha256:63199eb167a2c37df5cd40340c2665c894246eebd88aac8d0d39cb0fef351840"},
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-]
-
-[package.dependencies]
-ase = ">=3.23,<4.0"
-h5py = ">=3,<4"
-tqdm = ">=4,<5"
-typer = {version = ">=0,<1", extras = ["all"]}
-
-[[package]]
-name = "znjson"
-version = "0.2.6"
-description = "A Python Package to Encode/Decode some common file formats to json"
-optional = false
-python-versions = "<4.0,>=3.9"
-files = [
-    {file = "znjson-0.2.6-py3-none-any.whl", hash = "sha256:144cc842161cabf6bc97ec8908dd99b2ef37faa4ecc4083d1ad816654a5527e0"},
-    {file = "znjson-0.2.6.tar.gz", hash = "sha256:b9a5360bc598018ea82972c595b953edc6cc0b3f860fbaff46034266461372ea"},
-]
-
-[[package]]
-name = "zntrack"
-version = "0.8.0"
-description = "Create, Run and Benchmark DVC Pipelines in Python"
-optional = false
-python-versions = "<4,>=3.10"
-files = [
-    {file = "zntrack-0.8.0-py3-none-any.whl", hash = "sha256:8b1d3e98b026ad3312bddf7ef8cef6ba56455c542be0163629daf1214500a205"},
-    {file = "zntrack-0.8.0.tar.gz", hash = "sha256:5ad0dd4ec517880cb49ac794e0ff990b7dc2ba8395f1b3d9326998ecb69ebe97"},
-]
-
-[package.dependencies]
-dvc = ">=3.50,<4.0"
-pandas = ">=2,<3"
-pyyaml = ">=6,<7"
-tqdm = ">=4,<5"
-typer = ">=0.13,<0.14"
-znfields = ">=0.1.2,<0.2.0"
-znflow = ">=0.2.2,<0.3.0"
-znjson = ">=0.2,<0.3"
-
-[extras]
-zntrack = ["zntrack"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.10,<4.0"
-content-hash = "f2e510f487dfe2d186b79bad42c9c2b62abc291c750b2e89267b14f26351ab4d"
diff --git a/pyproject.toml b/pyproject.toml
index c0f7160..e0b9059 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,40 +1,40 @@
-[tool.poetry]
+[project]
 name = "erbs"
 version = "0.1.0"
 description = "Enhanced sampling in the representation space of atomistic neural networks."
-authors = ["Moritz R. Schäfer <schaefer@theochem.uni-stuttgart.de>"]
 keywords=["machine-learning", "interatomic potentials", "molecular-dynamics", "enhanced sampling"]
 license = "MIT"
+authors = [
+    { name = "Moritz René Schäfer", email = "schaefer@theochem.uni-stuttgart.de" },
+]
+
 readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "apax>=0.10.2",
+]
+
+[project.urls]
+Repository = "https://github.com/apax-hub/erbs"
+Releases = "https://github.com/apax-hub/erbs/releases"
+Discord = "https://discord.gg/7ncfwhsnm4"
 
-[tool.poetry.dependencies]
-python = ">=3.10,<4.0"
-numpy = "^1.26.0"
-ase = "^3.22.1"
-jax = "^0.4.35"
-flax = "^0.8.0"
-scikit-learn = "^1.3.0"
-einops = "^0.6.1"
-apax = "^0.9.1"
-zntrack = {version = "^0.8.0", optional = true}
-ipsuite = "^0.2.1"
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-[tool.poetry.extras]
-zntrack = ["zntrack"]
+[dependency-groups]
+dev = [
+    "pytest>=8.3.5",
+    "ruff>=0.11.9",
+]
 
+[tool.ruff]
+line-length = 90
 
-[tool.poetry.group.dev.dependencies]
-black = "^23.9.1"
-isort = "^5.12.0"
-flake8 = "^6.1.0"
-pytest = "^7.4.2"
-pytest-cov = "^4.1.0"
-coverage = "^7.3.1"
-sphinx = "^7.2.6"
-sphinx-rtd-theme = "^1.3.0"
-matplotlib = "^3.8.0"
-pre-commit = "^3.4.0"
+[tool.ruff.lint]
+select = ["E", "F", "C", "I", "E"]
+extend-ignore = [
+    "E501", "E731", "E741", "E402", "F841", "F811", "C901", "E203", "E402"
+]
 
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
diff --git a/run_md.py b/run_md.py
index e17ff6c..f973913 100644
--- a/run_md.py
+++ b/run_md.py
@@ -1,19 +1,17 @@
-from ase.io import read
-import numpy as np
-from ase.neighborlist import build_neighbor_list
 from ase import units
-from ase.md.verlet import VelocityVerlet
-from ase.md.langevin import Langevin
-
+from ase.io import read
 from ase.io.trajectory import Trajectory
-from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary, ZeroRotation
-from xtb.ase.calculator import XTB
-
 from ase.md.contour_exploration import ContourExploration
+from ase.md.velocitydistribution import (
+    MaxwellBoltzmannDistribution,
+    Stationary,
+    ZeroRotation,
+)
+from xtb.ase.calculator import XTB
 
 from erbs.transformations import repartition_hydrogen_mass
 
-atoms = read("raw_data/ala2.xyz") # etoh.traj
+atoms = read("raw_data/ala2.xyz")  # etoh.traj
 atoms = repartition_hydrogen_mass(atoms, 2.0)
 
 calc = XTB(method="gfn-ff")
@@ -22,30 +20,33 @@
 T = 300
 dt = 2.0
 # duration = 10_000
-friction=0.001
-remaining_steps = 10_000 #int(np.ceil(duration / dt))
+friction = 0.001
+remaining_steps = 10_000  # int(np.ceil(duration / dt))
 
 write_interval = 1
-traj_path = f"test_data/ala2_ce_strong_5.traj"
+traj_path = "test_data/ala2_ce_strong_5.traj"
 MaxwellBoltzmannDistribution(atoms, temperature_K=T)
 Stationary(atoms)
 ZeroRotation(atoms)
-# 
+#
 # dyn = VelocityVerlet(atoms, dt * units.fs)
 # dyn = Langevin(atoms, dt * units.fs, friction=friction, temperature_K=T)
 dyn = ContourExploration(atoms, energy_target=-92.9)
 
+
 def printenergy(a=atoms):  # store a reference to atoms in the definition.
     """Function to print the potential, kinetic and total energy."""
     epot = a.get_potential_energy() / len(a)
     ekin = a.get_kinetic_energy() / len(a)
-    print('Energy per atom: Epot = %.3feV  Ekin = %.3feV (T=%3.0fK)  '
-          'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))
+    print(
+        "Energy per atom: Epot = %.3feV  Ekin = %.3feV (T=%3.0fK)  "
+        "Etot = %.3feV" % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin)
+    )
+
 
 # Now run the dynamics
 
-traj = Trajectory(traj_path, 'w', atoms)
+traj = Trajectory(traj_path, "w", atoms)
 dyn.attach(printenergy, interval=500)
 dyn.attach(traj.write, interval=write_interval)
 dyn.run(remaining_steps)
-
diff --git a/tests/unit_tests/cv/test_cv_nl.py b/tests/unit_tests/cv/test_cv_nl.py
index 62e1b78..1da1c64 100644
--- a/tests/unit_tests/cv/test_cv_nl.py
+++ b/tests/unit_tests/cv/test_cv_nl.py
@@ -4,9 +4,9 @@
 
 
 def test_compute_cv_nl():
-    Z = np.array([1,1,2])
-    Z_ref = np.array([2,1,2,1])
+    Z = np.array([1, 1, 2])
+    Z_ref = np.array([2, 1, 2, 1])
 
     nl = compute_cv_nl(Z, Z_ref)
 
-    assert nl.shape == [2,6]
\ No newline at end of file
+    assert nl.shape == [2, 6]
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..989af03
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2197 @@
+version = 1
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.2.2"
+source = { registry = "https://pypi.org/simple" }
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+version = "0.7.0"
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+    { name = "clu" },
+    { name = "e3x" },
+    { name = "einops" },
+    { name = "flax" },
+    { name = "jax" },
+    { name = "lazy-loader" },
+    { name = "numpy" },
+    { name = "optax" },
+    { name = "orbax-checkpoint" },
+    { name = "pydantic" },
+    { name = "tensorflow" },
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