Merge pull request #141 from NLESC-JCER/paper

Paper

.github/workflows/draft-pdf.yml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

bin/qmctorch

@@ -3,7 +3,7 @@ import os
 import argparse
 
 
-dist_solvers = ['SolverSlaterJastrowHorovod']
+dist_solvers = ['SolverMPI']
 
 
 def check_file(fname):

docs/conf.py

@@ -105,7 +105,8 @@
     'sphinx.ext.mathjax',
     'sphinx.ext.ifconfig',
     'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode'
+    'sphinx.ext.viewcode',
+    'nbsphinx'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -139,7 +140,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -165,7 +166,7 @@
 #     html_theme = 'classic'
 
 html_theme = 'sphinx_rtd_theme'
-html_logo = "qmctorch_white.png"
+html_logo = "./pics/qmctorch_white.png"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -261,3 +262,4 @@
 
 autoclass_content = 'init'
 autodoc_member_order = 'bysource'
+nbsphinx_allow_errors = True

docs/example/colab/google_collab.ipynb

@@ -0,0 +1 @@
+{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"colab_type":"text","id":"PA9WOB4zJfCu"},"source":["# Installation on Google Collab\n","To install `QMCTorch` copy the code from one of the 3 text cells below in a code cell and run that cell. "]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"6KLOhN_dGQ11"},"source":["## Install QMCTorch from Pypi Package manager\n","\n","```\n","! pip install qmctorch\n","```\n","\n"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Eb9wI3eOGfz1"},"source":["## Install QMCTorch from GitHub\n","```\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","% cd gdrive/My Drive/\n","! git clone https://github.com/NLESC-JCER/QMCTorch\n","% cd QMCTorch\n","! pip install -e .\n","% cd ../\n","```"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"_VNw5sAeHC7M"},"source":["## Pull latest code from Github\n","```\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","% cd gdrive/My Drive/QMCTorch\n","! git pull origin master\n","! pip install -e .\n","% cd ../\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":671},"colab_type":"code","executionInfo":{"elapsed":9770,"status":"ok","timestamp":1588617088336,"user":{"displayName":"Nicolas Renaud","photoUrl":"","userId":"07120063468244602126"},"user_tz":-120},"id":"khGd1-ewHZWF","outputId":"674fbe9e-7817-4e11-cc91-cd508bda8107","vscode":{"languageId":"python"}},"outputs":[],"source":["# from google.colab import drive\n","# drive.mount('/content/gdrive')\n","# % cd gdrive/My Drive/QMCTorch\n","# ! git pull origin master\n","# ! pip install -e .\n","# % cd .."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"MGNu_L-OJ-7u"},"source":["# Using QMCTorch"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"colab_type":"code","executionInfo":{"elapsed":11632,"status":"ok","timestamp":1588617090211,"user":{"displayName":"Nicolas Renaud","photoUrl":"","userId":"07120063468244602126"},"user_tz":-120},"id":"p7qEQTV2HB4h","outputId":"52c2bbce-3775-442c-95de-d18bd432c2e3","vscode":{"languageId":"python"}},"outputs":[],"source":["import torch\n","from torch import optim\n","from qmctorch.scf import Molecule\n","from qmctorch.wavefunction import SlaterJastrow\n","from qmctorch.solver import Solver\n","from qmctorch.sampler import Metropolis\n","from qmctorch.utils import set_torch_double_precision\n","from qmctorch.utils import plot_energy, plot_data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"x-s06JyaHUdN","vscode":{"languageId":"python"}},"outputs":[],"source":["use_gpu = torch.cuda.is_available()\n","set_torch_double_precision()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"colab_type":"code","executionInfo":{"elapsed":11608,"status":"ok","timestamp":1588617090213,"user":{"displayName":"Nicolas Renaud","photoUrl":"","userId":"07120063468244602126"},"user_tz":-120},"id":"7HO4cNaAID-F","outputId":"a1f652aa-9bcb-4e9b-c038-833b29da2eae","vscode":{"languageId":"python"}},"outputs":[],"source":["mol = Molecule(atom='H 0 0 0.69; H 0 0 -0.69', unit='bohr', \\\n","               calculator='pyscf', basis='sto-3g')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"hFZg-XanIOeY","vscode":{"languageId":"python"}},"outputs":[],"source":["wf = SlaterJastrow(mol, configs='cas(2,2)', cuda=use_gpu)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"Mqlk1N3tIVXN","vscode":{"languageId":"python"}},"outputs":[],"source":["sampler = Metropolis(nwalkers=2000, nstep=2000, step_size=0.2, \\\n","                     ntherm=-1, ndecor=100, nelec=wf.nelec, \\\n","                     init=mol.domain('atomic'), \\\n","                     move={'type':'all-elec', 'proba':'normal'},\n","                     cuda=use_gpu)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"5-I7abLxI5qG","vscode":{"languageId":"python"}},"outputs":[],"source":["lr_dict = [{'params': wf.jastrow.parameters(), 'lr': 3E-3},\n","           {'params': wf.ao.parameters(), 'lr': 1E-6},\n","           {'params': wf.mo.parameters(), 'lr': 1E-3},\n","           {'params': wf.fc.parameters(), 'lr': 2E-3}]\n","opt = optim.Adam(lr_dict, lr=1E-3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"xgXSp8JwJIr9","vscode":{"languageId":"python"}},"outputs":[],"source":["scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.90)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"g6TE--nNJL1H","vscode":{"languageId":"python"}},"outputs":[],"source":["solver = Solver(wf=wf, sampler=sampler,\n","                       optimizer=opt, scheduler=scheduler)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":87},"colab_type":"code","executionInfo":{"elapsed":26805,"status":"ok","timestamp":1588617105475,"user":{"displayName":"Nicolas Renaud","photoUrl":"","userId":"07120063468244602126"},"user_tz":-120},"id":"Y5MPLiv2JTCy","outputId":"ef067f86-11b3-48e0-9dac-dc8ff5784905","vscode":{"languageId":"python"}},"outputs":[],"source":["# obs = solver.single_point()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"yfx4g1Luz9Z-","vscode":{"languageId":"python"}},"outputs":[],"source":["# solver.configure(freeze=['ao', 'mo'], loss='energy', grad='manual')\n","# solver.track_observable(['local_energy'])\n","\n","# solver.configure_resampling(mode='update',\n","#                             resample_every=1,\n","#                             nstep_update=50)\n","# solver.ortho_mo = False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","executionInfo":{"elapsed":52595,"status":"ok","timestamp":1588617131289,"user":{"displayName":"Nicolas Renaud","photoUrl":"","userId":"07120063468244602126"},"user_tz":-120},"id":"VtrVSk620A1A","outputId":"32ad9904-40e5-4efa-bcee-27145e04cfd4","vscode":{"languageId":"python"}},"outputs":[],"source":["# obs = solver.run(50)"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyMr6DKmNBGlyg+0pzpuupgz","name":"qmctorch.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}

example/gpu/h2.py → docs/example/gpu/h2.py

@@ -2,7 +2,7 @@
 
 from qmctorch.scf import Molecule
 from qmctorch.wavefunction import SlaterJastrow
-from qmctorch.solver import SolverSlaterJastrow
+from qmctorch.solver import Solver
 from qmctorch.sampler import Metropolis
 from qmctorch.utils import set_torch_double_precision
 from qmctorch.utils import (plot_energy, plot_data)
@@ -45,7 +45,7 @@
 scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.90)
 
 # QMC solver
-solver = SolverSlaterJastrow(wf=wf, sampler=sampler,
+solver = Solver(wf=wf, sampler=sampler,
                              optimizer=opt, scheduler=None)
 
 # perform a single point calculation

example/horovod/h2.py → docs/example/horovod/h2.py

@@ -4,7 +4,7 @@
 
 from qmctorch.scf import Molecule
 from qmctorch.wavefunction import SlaterJastrow
-from qmctorch.solver import SolverSlaterJastrowHorovod
+from qmctorch.solver import SolverMPI
 from qmctorch.sampler import Metropolis
 from qmctorch.utils import set_torch_double_precision
 from qmctorch.utils import (plot_energy, plot_data)
@@ -15,7 +15,8 @@
 # bond dissociation energy 4.478 eV -> 0.16 hartree
 
 hvd.init()
-if torch.cuda.is_available():
+use_cuda = torch.cuda.is_available()
+if use_cuda:
     torch.cuda.set_device(hvd.rank())
 
 set_torch_double_precision()
@@ -29,14 +30,15 @@
 # define the wave function
 wf = SlaterJastrow(mol, kinetic='jacobi',
                    configs='cas(2,2)',
-                   cuda=False)
+                   cuda=use_cuda)
 
 # sampler
 sampler = Metropolis(nwalkers=200,
                      nstep=200, step_size=0.2,
                      ntherm=-1, ndecor=100,
                      nelec=wf.nelec, init=mol.domain('atomic'),
-                     move={'type': 'all-elec', 'proba': 'normal'})
+                     move={'type': 'all-elec', 'proba': 'normal'},
+                     cuda=use_cuda)
 
 
 # optimizer
@@ -50,7 +52,7 @@
 scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.90)
 
 # QMC solver
-solver = SolverSlaterJastrowHorovod(wf=wf, sampler=sampler,
+solver = SolverMPI(wf=wf, sampler=sampler,
                                     optimizer=opt, scheduler=scheduler,
                                     rank=hvd.rank())
 

example/optimization/h2.py → docs/example/optimization/h2.py

@@ -3,8 +3,8 @@
 
 from qmctorch.scf import Molecule
 from qmctorch.wavefunction import SlaterJastrow
-from qmctorch.solver import SolverSlaterJastrow
-from qmctorch.sampler import Metropolis, Hamiltonian
+from qmctorch.solver import Solver
+from qmctorch.sampler import Metropolis
 from qmctorch.utils import set_torch_double_precision
 from qmctorch.utils import (plot_energy, plot_data)
 
@@ -26,43 +26,43 @@
 # define the wave function
 wf = SlaterJastrow(mol, kinetic='jacobi',
                    configs='single_double(2,2)',
-                   jastrow_kernel=PadeJastrowKernel)
+                   jastrow_kernel=PadeJastrowKernel).gto2sto()
 
 # sampler
-sampler = Hamiltonian(nwalkers=100, nstep=100, nelec=wf.nelec,
-                      step_size=0.1, L=30,
-                      ntherm=-1, ndecor=10,
-                      init=mol.domain('atomic'))
-
+sampler = Metropolis(nwalkers=5000,
+                     nstep=200, step_size=0.2,
+                     ntherm=-1, ndecor=100,
+                     nelec=wf.nelec, init=mol.domain('atomic'),
+                     move={'type': 'all-elec', 'proba': 'normal'})
 
 # optimizer
-lr_dict = [{'params': wf.jastrow.parameters(), 'lr': 3E-3},
+lr_dict = [{'params': wf.jastrow.parameters(), 'lr': 1E-2},
            {'params': wf.ao.parameters(), 'lr': 1E-6},
-           {'params': wf.mo.parameters(), 'lr': 1E-3},
+           {'params': wf.mo.parameters(), 'lr': 2E-3},
            {'params': wf.fc.parameters(), 'lr': 2E-3}]
 opt = optim.Adam(lr_dict, lr=1E-3)
 
 # scheduler
-scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.90)
+scheduler = optim.lr_scheduler.StepLR(opt, step_size=10, gamma=0.90)
 
 # QMC solver
-solver = SolverSlaterJastrow(wf=wf, sampler=sampler,
+solver = Solver(wf=wf, sampler=sampler,
                              optimizer=opt, scheduler=None)
 
 # perform a single point calculation
 obs = solver.single_point()
 
 # configure the solver
-solver.configure(track=['local_energy'], freeze=['ao', 'mo'],
-                 loss='energy', grad='auto',
+solver.configure(track=['local_energy', 'parameters'], freeze=['ao'],
+                 loss='energy', grad='manual',
                  ortho_mo=False, clip_loss=False,
                  resampling={'mode': 'update',
                              'resample_every': 1,
-                             'nstep_update': 50})
+                             'nstep_update': 25})
 
 # optimize the wave function
-obs = solver.run(250)
+obs = solver.run(50)
 
 # plot
 plot_energy(obs.local_energy, e0=-1.1645, show_variance=True)
-plot_data(solver.observable, obsname='jastrow.weight')
+plot_data(solver.observable, obsname='jastrow.jastrow_kernel.weight')

docs/example/scf/scf.py

@@ -0,0 +1,23 @@
+from qmctorch.scf import Molecule
+
+# Select the SCF calculator
+calc = ['pyscf',            # pyscf  
+        'adf',          # adf 2019 
+        'adf2019'               # adf 2020+
+    ][1]
+
+# select an appropriate basis
+basis = {
+    'pyscf'  : 'sto-6g',
+    'adf'    : 'VB1',
+    'adf2019': 'dz'
+}[calc]
+
+# do the scf calculation
+mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
+            calculator=calc,
+            basis=basis,
+            unit='bohr')
+
+
+

example/single_point/h2o_sampling.py → docs/example/single_point/h2o_sampling.py

@@ -1,7 +1,7 @@
 from qmctorch.scf import Molecule
 from qmctorch.wavefunction import SlaterJastrow
 from qmctorch.sampler import Metropolis
-from qmctorch.solver import SolverSlaterJastrow
+from qmctorch.solver import Solver
 from qmctorch.utils import plot_walkers_traj
 
 # define the molecule
@@ -16,10 +16,10 @@
 sampler = Metropolis(nwalkers=100, nstep=500, step_size=0.25,
                      nelec=wf.nelec, ndim=wf.ndim,
                      init=mol.domain('atomic'),
-                     move={'type': 'one-elec', 'proba': 'normal'})
+                     move={'type': 'all-elec', 'proba': 'normal'})
 
 # solver
-solver = SolverSlaterJastrow(wf=wf, sampler=sampler)
+solver = Solver(wf=wf, sampler=sampler)
 
 # single point
 obs = solver.single_point()

docs/index.rst

@@ -10,35 +10,30 @@ Quantum Monte Carlo with Pytorch
    :maxdepth: 1
    :caption: QMCTorch
 
-   intro
-   qmc
-   qmctorch/molecule
-   qmctorch/wavefunction
-   qmctorch/sampler
-   qmctorch/optimizer
-   qmctorch/solver
+   rst/install
+   rst/qmc
+   rst/qmctorch
+   rst/hdf5
 
 .. toctree::
    :maxdepth: 1
    :caption: Tutorial
 
-   tutorial/tutorial_sampling_traj
-   tutorial/tutorial_correlation
-   tutorial/tutorial_wf_opt
-   tutorial/tutorial_jastrow
-   tutorial/tutorial_backflow
-   # tutorial/tutorial_geo_opt
-   tutorial/tutorial_gpus
-   tutorial/tutorial_hdf5
+   notebooks/molecule
+   notebooks/sampling
+   notebooks/wfopt 
+   notebooks/geoopt 
+   notebooks/gpu
 
 .. toctree::
    :maxdepth: 1
-   :caption: Examples
+   :caption: Advanced Tutorial
+
+   notebooks/correlation
+   notebooks/create_jastrow
+   notebooks/create_backflow
+   notebooks/horovod
 
-   example/example_sp
-   example/example_opt
-   example/example_gpu
-   example/example_horovod
 
 .. toctree::
    :maxdepth: 1