Skip to content

Latest commit

 

History

History
93 lines (72 loc) · 2.98 KB

README.md

File metadata and controls

93 lines (72 loc) · 2.98 KB
Raw

torch-dftd

pytorch implementation of dftd2 [1] & dftd3 [2, 3]

Install

# Install from pypi
pip install torch-dftd

# Install from source (for developers)
git clone https://github.com/pfnet-research/torch-dftd
pip install -e .

Quick start

from ase.build import molecule
from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator

atoms = molecule("CH3CH2OCH3")
# device="cuda:0" for fast GPU computation.
calc = TorchDFTD3Calculator(atoms=atoms, device="cpu", damping="bj")

energy = atoms.get_potential_energy()
forces = atoms.get_forces()

print(f"energy {energy} eV")
print(f"forces {forces}")

Dependency

The library is tested under following environment.

  • python: 3.10
  • CUDA: 12.2
torch==2.0.1
ase==3.22.1
# Below is only for 3-body term
cupy-cuda12x==12.2.0
pytorch-pfn-extras==0.7.3

Development tips

Formatting & Linting

pysen is used to format the python code of this repository.
You can simply run below to get your code formatted :)

# Format the code
$ pysen run format
# Check the code format
$ pysen run lint

CUDA Kernel function implementation with cupy

cupy supports users to implement CUDA kernels within python code, and it can be easily linked with pytorch tensor calculations.
Element wise kernel is implemented and used in some pytorch functions to accelerate speed with GPU.

See document for details about user defined kernel.

Citation

Please always cite original paper of DFT-D2 [1] or DFT-D3 [2, 3].
Also, please cite the paper [4] if you used this software for your publication.

DFT-D2:
[1] S. Grimme, J. Comput. Chem, 27 (2006), 1787-1799. DOI: 10.1002/jcc.20495

DFT-D3:
[2] S. Grimme, J. Antony, S. Ehrlich and H. Krieg, J. Chem. Phys, 132 (2010), 154104. DOI: 10.1063/1.3382344

If BJ-damping is used in DFT-D3:
[3] S. Grimme, S. Ehrlich and L. Goerigk, J. Comput. Chem, 32 (2011), 1456-1465. DOI: 10.1002/jcc.21759

[4] PFP: Universal Neural Network Potential for Material Discovery

@misc{takamoto2021pfp,
      title={PFP: Universal Neural Network Potential for Material Discovery}, 
      author={So Takamoto and Chikashi Shinagawa and Daisuke Motoki and Kosuke Nakago and Wenwen Li and Iori Kurata and Taku Watanabe and Yoshihiro Yayama and Hiroki Iriguchi and Yusuke Asano and Tasuku Onodera and Takafumi Ishii and Takao Kudo and Hideki Ono and Ryohto Sawada and Ryuichiro Ishitani and Marc Ong and Taiki Yamaguchi and Toshiki Kataoka and Akihide Hayashi and Takeshi Ibuka},
      year={2021},
      eprint={2106.14583},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci}
}