StrainNet code is an implementation of a paper StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks.
StrainNet can be employed to train and/or predict a strain energy density in a unit of eV/atom. 21 strain energy density of each crystal structure will be predicted and can be converted to an elastic tensor.
training command:
python train.py --config_path conf/config.yaml --out_dir output
predicting command:
python predict.py --ckpt_dir output --json_path path-to-json-file
The SE(3)-Transformers code has been adopted with some modifications from SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks.
Please cite them as
@inproceedings{fuchs2020se3transformers,
title={SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks},
author={Fabian B. Fuchs and Daniel E. Worrall and Volker Fischer and Max Welling},
year={2020},
booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS)},
}