Deep learning quantum Monte Carlo for electrons in real space

DeepQMC

DeepQMC implements variational quantum Monte Carlo for electrons in molecules, using deep neural networks written in PyTorch 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.

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

A simple example

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