NepTrainKit documentation

NepTrainKit is a toolkit focused on the operation and visualization of neuroevolution potential (NEP) training datasets. It is mainly used to simplify and optimize the NEP model training process, providing an intuitive graphical interface and analysis tools to help users adjust train dataset.

Note

Installing via pip auto-detects CUDA. If a compatible CUDA toolkit is available, the NEP backend is compiled with GPU acceleration; otherwise, a CPU-only backend is built. If CUDA is not detected automatically, set one of CUDA_HOME or CUDA_PATH and update your loader path before running pip install.

Linux/WSL2 example:

export CUDA_HOME=/usr/local/cuda-12.4
export PATH="$CUDA_HOME/bin:$PATH"
export LD_LIBRARY_PATH="$CUDA_HOME/lib64:${LD_LIBRARY_PATH}"
pip install NepTrainKit

Windows (PowerShell) example:

$env:CUDA_PATH = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.4"
$env:Path = "$env:CUDA_PATH\\bin;" + $env:Path
pip install NepTrainKit

Citing NepTrainKit

If you rely on NepTrainKit for published research, please cite the following article and acknowledge the upstream NEP projects where appropriate:

@article{CHEN2025109859,
title = {NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials},
journal = {Computer Physics Communications},
volume = {317},
pages = {109859},
year = {2025},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2025.109859},
url = {https://www.sciencedirect.com/science/article/pii/S0010465525003613},
author = {Chengbing Chen and Yutong Li and Rui Zhao and Zhoulin Liu and Zheyong Fan and Gang Tang and Zhiyong Wang},
}

Documentation: