DeePMD-kit v2: A software package for Deep Potential models
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.
Keywords
Cite
@article{arxiv.2304.09409,
title = {DeePMD-kit v2: A software package for Deep Potential models},
author = {Jinzhe Zeng and Duo Zhang and Denghui Lu and Pinghui Mo and Zeyu Li and Yixiao Chen and Marián Rynik and Li'ang Huang and Ziyao Li and Shaochen Shi and Yingze Wang and Haotian Ye and Ping Tuo and Jiabin Yang and Ye Ding and Yifan Li and Davide Tisi and Qiyu Zeng and Han Bao and Yu Xia and Jiameng Huang and Koki Muraoka and Yibo Wang and Junhan Chang and Fengbo Yuan and Sigbjørn Løland Bore and Chun Cai and Yinnian Lin and Bo Wang and Jiayan Xu and Jia-Xin Zhu and Chenxing Luo and Yuzhi Zhang and Rhys E. A. Goodall and Wenshuo Liang and Anurag Kumar Singh and Sikai Yao and Jingchao Zhang and Renata Wentzcovitch and Jiequn Han and Jie Liu and Weile Jia and Darrin M. York and Weinan E and Roberto Car and Linfeng Zhang and Han Wang},
journal= {arXiv preprint arXiv:2304.09409},
year = {2023}
}
Comments
51 pages, 2 figures