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.
- Python 3.10.9 or higher
- Tensorflow-gpu
- FINUFFT (pytorch or 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.
@article{ji2025SOGNet,
title = {Machine-Learning Interatomic Potentials for Long-Range Systems},
author = {Ji, Yajie and Liang, Jiuyang and Xu, Zhenli},
journal = {Phys. Rev. Lett.},
volume = {135},
issue = {17},
pages = {178001},
numpages = {8},
year = {2025},
month = {Oct},
publisher = {American Physical Society},
doi = {10.1103/ssp9-7s81},
url = {https://link.aps.org/doi/10.1103/ssp9-7s81}
}
For any queries regarding SOG-Net, please contact Yajie Ji (jiyajie595@sjtu.edu.cn) or Jiuyang Liang (jliang@flatironinstitute.org).