teng-martin-ma / simple-nn_rop Goto Github PK

SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab initio data. The package provides an interfacing module to LAMMPS for MD simulations.

• Training over total energies, forces, and stresses.
• Symmetry function vectors for atomic features.
• Supports LAMMPS for MD simulations.
• PCA matrix transformation and whitening of training data for fast and accurate learning.
• Supports GPU via PyTorch.
• CPU parallelization of preprocessing training data via MPI for Python
• Uniform training to rectify sample bias (W. Jeong et al. J. Phys. Chem. C 122, 22790 (2018)).
• Replica ensemble for uncertainty estimation (W. Jeong et al. J. Phys. Chem. Lett. 11, 6090 (2020)).
• Compatible with results of most ab initio codes such as Quantum-Espresso and VASP via ASE module.
• Dropout technique for regularizing neural networks.
• Requires Python 3.6-3.9 and LAMMPS (23Jun2022 or newer)

Installation, manual, and full details: https://simple-nn-v2.readthedocs.io

If you use SIMPLE-NN, please cite:
K. Lee, D. Yoo, W. Jeong, and S. Han, "SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials", Comp. Phys. Comm. 242, 95 (2019) https://doi.org/10.1016/j.cpc.2019.04.014.