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https://github.com/animesh/deepqmc

Deep learning quantum Monte Carlo for electrons in real space

https://github.com/animesh/deepqmc

Science Score: 13.0%

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    Found 4 DOI reference(s) in README
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    Low similarity (10.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Deep learning quantum Monte Carlo for electrons in real space

Basic Info
  • Host: GitHub
  • Owner: animesh
  • License: mit
  • Default Branch: master
  • Homepage:
  • Size: 57.7 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Fork of deepqmc/deepqmc
Created over 5 years ago · Last pushed over 5 years ago

https://github.com/animesh/deepqmc/blob/master/ 

# DeepQMC

[![build](https://img.shields.io/travis/com/deepqmc/deepqmc/master.svg)](https://travis-ci.com/deepqmc/deepqmc)
[![coverage](https://img.shields.io/codecov/c/github/deepqmc/deepqmc.svg)](https://codecov.io/gh/deepqmc/deepqmc)
![python](https://img.shields.io/pypi/pyversions/deepqmc.svg)
[![pypi](https://img.shields.io/pypi/v/deepqmc.svg)](https://pypi.org/project/deepqmc/)
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[![license](https://img.shields.io/github/license/deepqmc/deepqmc.svg)](https://github.com/deepqmc/deepqmc/blob/master/LICENSE)
[![code style](https://img.shields.io/badge/code%20style-black-202020.svg)](https://github.com/ambv/black)
[![chat](https://img.shields.io/gitter/room/deepqmc/deepqmc)](https://gitter.im/deepqmc/deepqmc)
[![doi](https://img.shields.io/badge/doi-10.5281%2Fzenodo.3960826-blue)](http://doi.org/10.5281/zenodo.3960826)

DeepQMC implements variational quantum Monte Carlo for electrons in molecules, using deep neural networks written in [PyTorch](https://pytorch.org) as trial wave functions. Besides the core functionality, it contains implementations of the following ansatzes:

- PauliNet: https://doi.org/10.1038/s41557-020-0544-y

## Installing

Install and update using [Pip](https://pip.pypa.io/en/stable/quickstart/).

```
pip install -U deepqmc[wf,train,cli]
```

## A simple example

```python
from deepqmc import Molecule, evaluate, train
from deepqmc.wf import PauliNet

mol = Molecule.from_name('LiH')
net = PauliNet.from_hf(mol).cuda()
train(net)
evaluate(net)
```

Or on the command line:

```
$ cat lih/param.toml
system = 'LiH'
ansatz = 'paulinet'
[train_kwargs]
n_steps = 40
$ deepqmc train lih --save-every 20
converged SCF energy = -7.9846409186467
equilibrating: 49it [00:07,  6.62it/s]
training: 100%|| 40/40 [01:30<00:00,  2.27s/it, E=-8.0302(29)]
$ ln -s chkpts/state-00040.pt lih/state.pt
$ deepqmc evaluate lih
evaluating:  24%|  | 136/565 [01:12<03:40,  1.65it/s, E=-8.0396(17)]
```

## Links

- Documentation: https://deepqmc.github.io

Owner

  • Name: Ani
  • Login: animesh
  • Kind: user
  • Location: Norway
  • Company: Norwegian University of Science and Technology

A medical graduate from Delhi University with post-graduation in bioinformatics from Jawaharlal Nehru University, India.

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