Sum-of-Gaussians Neural Network (SOG-Net): A Machine-Learning Interatomic Potential for Long-Range Systems
Sum-of-Gaussians Neural Network (SOG-Net) is a lightweight and versatile framework for integrating long-range interactions into machine learning force field. The SOG-Net employs a latent-variable learning network that seamlessly bridges short-range and long-range components, coupled with an efficient Fourier convolution layer that incorporates long-range effects. By learning sum-of-Gaussians multipliers across different convolution layers, the SOG-Net adaptively captures diverse long-range decay behaviors while maintaining close-to-linear computational complexity during training and simulation via non-uniform fast Fourier transforms.
Authors: Yajie Ji, Jiuyang Liang, Zhenli Xu.
Paper Links: ArXiv
- Python 3.10.9 or higher
- Tensorflow-gpu
- FINUFFT (tensorflow version)
- ASE (Atomic Simulation Environment)
Please refer to the setup.py file for installation instructions.
Example scripts can be found in \Deep-SOG\examples and each numerical example folder in \CACE-SOG, which are based on the DeepMD short-range descriptor and the CACE descriptor, respectively.
This project is licensed under the MIT License.
@misc{ji2025machinelearninginteratomicpotentialslongrange,
title={Machine-Learning Interatomic Potentials for Long-Range Systems},
author={Yajie Ji and Jiuyang Liang and Zhenli Xu},
year={2025},
eprint={2502.04668},
archivePrefix={arXiv},
primaryClass={physics.chem-ph},
url={https://arxiv.org/abs/2502.04668},
}
For any queries regarding SOG-Net, please contact Yajie Ji (jiyajie595@sjtu.edu.cn) or Jiuyang Liang (jliang@flatironinstitute.org).