DeepQMC implements variational quantum Monte Carlo for electrons in molecules, using deep neural networks as trial wave functions. The package is based on JAX and Haiku. Besides the core functionality, it contains an implementation of a flexible neural network wave function ansatz, that can be configured to obtain a broad range of molecular neural network wave functions. Config files for the instantiation of variants of PauliNet, FermiNet and DeepErwin can be found under src/deepqmc/conf/ansatz.
Install and update to the latest release using Pip:
pip install -U deepqmc
To install DeepQMC from a local Git repository run:
git clone https://github.com/deepqmc/deepqmc
cd deepqmc
pip install -e .[dev]
If Pip complains about setup.py not being found, please update to the latest Pip version.
The above installation will result in the CPU version of JAX. However, running DeepQMC on the GPU is highly recomended. To enable GPU support make sure to upgrade JAX to match the CUDA and cuDNN versions of your system. For most users this can be achieved with:
# CUDA 12 installation
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
# CUDA 11 installation
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
If issues arise during the JAX installation visit the JAX Install Guide.
For further information about the DeepQMC package and tutorials covering the basic usage visit the documentation.
This repository can be cited as:
@software{deepqmc,
author = {Jan Hermann and
Zeno Schätzle and
Peter Bernát Szabó and
Matěj Mezera and
{DeepQMC Contributers}},
title = {{DeepQMC}},
year = {2023},
publisher = {Zenodo},
copyright = {MIT},
url = {https://github.com/deepqmc/deepqmc},
doi = {10.5281/zenodo.7503172},
}