DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Abstract
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.
Cite
@article{arxiv.2502.19161,
title = {DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials},
author = {Jinzhe Zeng and Duo Zhang and Anyang Peng and Xiangyu Zhang and Sensen He and Yan Wang and Xinzijian Liu and Hangrui Bi and Yifan Li and Chun Cai and Chengqian Zhang and Yiming Du and Jia-Xin Zhu and Pinghui Mo and Zhengtao Huang and Qiyu Zeng and Shaochen Shi and Xuejian Qin and Zhaoxi Yu and Chenxing Luo and Ye Ding and Yun-Pei Liu and Ruosong Shi and Zhenyu Wang and Sigbjørn Løland Bore and Junhan Chang and Zhe Deng and Zhaohan Ding and Siyuan Han and Wanrun Jiang and Guolin Ke and Zhaoqing Liu and Denghui Lu and Koki Muraoka and Hananeh Oliaei and Anurag Kumar Singh and Haohui Que and Weihong Xu and Zhangmancang Xu and Yong-Bin Zhuang and Jiayu Dai and Timothy J. Giese and Weile Jia and Ben Xu and Darrin M. York and Linfeng Zhang and Han Wang},
journal= {arXiv preprint arXiv:2502.19161},
year = {2025}
}