Flexible, general-purpose VMC framework, built on JAX.
Python 3.9 is required, and a virtual environment is recommended. After cloning, use pip install -e . to install the package in editable/develop mode, or pip install -e .[testing] if planning on running tests.
If running on a GPU, CUDA needs to be set up properly to work with JAX, and you will need to install the correct jaxlib wheel. See, e.g., https://github.com/google/jax#installation.
This repository is built to serve two purposes:
- Provide a general python API for variational Monte Carlo calculations compatible with JAX, with a number of built-in neural network architectures for ready-use.
- Provide a command-line interface exposing a large number of options for more streamlined (but somewhat less custom) experimentation with architecture/optimization/sampling hyperparameters.
This repository was built as a JAX port of an internal TensorFlow project started in 2019 which itself was initially inspired by the work of David Pfau, James S. Spencer, Alexander G. D. G. Matthews, and W. M. C. Foulkes. Their repository (and its own JAX branch) can be found here.
The primary routine exposed by this repository which implements the VMC training loop is the train.vmc.vmc_loop function. This function implements a very generic unsupervised training loop. A skeleton of a script which performs varational Monte Carlo would look something like:
import jax
import vmcnet.mcmc as mcmc
import vmcnet.train as train
# Training hyperparameters
nchains = ...
nburn = ...
nepochs = ...
seed = ...
logdir = ...
checkpoint_every = ...
checkpoint_dir = ...
# Initialize parameters, data, and optimization state
params = ...
data = ...
optimizer_state = ...
# Walker function (get new data)
def walker_fn(params, data, key):
...
return accept_ratio, data, key
# Define how the parameters are updated
def update_param_fn(params, data, optimizer_state, key):
...
return params, optimizer_state, metrics, key
# (Optionally) burn samples
def burning_step(params, data, key):
...
return data, key
key = jax.random.PRNGKey(seed)
data, key = mcmc.metropolis.burn_data(burning_step, nburn, params, data, key)
# Train!
params, optimizer_state, data, key = train.vmc.vmc_loop(
params,
optimizer_state,
data,
nchains,
nepochs,
walker_fn,
update_param_fn,
key,
logdir,
checkpoint_every=checkpoint_every,
checkpoint_dir=checkpoint_dir,
)Note the required function signatures. A simple but complete working example can be found in the hydrogen-like atom example in the test suite.
Alternatively, a command-line interface has been implemented which provides more streamlined access to subsets of the repository via setting ConfigDict objects.
There are two scripts which have been exposed thus far: vmc-molecule and vmc-statistics.
The primary command vmc-molecule calls train.runners.run_molecule. See train.default_config.get_default_config() to explore the options which have been exposed to the command-line. To edit these options at the command-line, use the "--config." prefix. For example,
vmc-molecule \
--config.problem.ion_pos="((0.0, 0.0, -2.0), (0.0, 0.0, 2.0))" \
--config.problem.ion_charges="(7.0, 7.0)" \
--config.problem.nelec="(7, 7)" \
--config.model.ferminet.full_det="True" \
--config.logging_level="INFO"will train the full single-determinant FermiNet on the nitrogen molecule at dissociating bond length 4.0 for 2e5 epochs on 2000 walkers (which are distributed across any available GPUs, if supported by the installation).
You can also reload and evaluate or continue training from previous checkpoints via the "--reload." prefix. The options can be seen in train.default_config.get_default_reload_config(). The reloading will only occur if --reload.logdir is set.
The vmc-statistics command calls train.runners.vmc_statistics. This simple script is designed to be compatible with the output of an evaluation run with vmc-molecule, but can accept any path to a file which contains local energies (a file with nchains x nepochs energies). It computes and saves a json file containing the average energy, the sample variance, the estimated integrated autocorrelation, and the estimated standard error. The options can be viewed simply via vmc-statistics -h.
See how to contribute.
A preprint of the paper which originally introduced this repository can be found at https://arxiv.org/abs/2112.03491, which can be cited via:
@misc{lin2021explicitly,
title={Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation},
author={Jeffmin Lin and Gil Goldshlager and Lin Lin},
year={2021},
eprint={2112.03491},
archivePrefix={arXiv},
primaryClass={physics.comp-ph}
}
If citing version 0.1.0 of this GitHub repository directly, you can use the following citation:
@software{vmcnet2021github,
author = {Jeffmin Lin and Gil Goldshlager and Lin Lin},
title = {{VMCNet}: Flexible, general-purpose {VMC} framework, built on {JAX}},
url = {http://github.com/jeffminlin/vmcnet},
version = {0.1.0},
year = {2021},
}