作者
Jinzhe Zeng,Duo Zhang,Denghui Lu,Pinghui Mo,Zeyu Li,Yixiao Chen,Marián Rynik,Li'ang Huang,Ziyao Li,Shaochen Shi,Yingze Wang,Haotian Ye,Ping Tuo,Jiabin Yang,Ding Ye,Yifan Li,Davide Tisi,Qiyu Zeng,Baohui Han,Yu Lei,Jiayuan Huang,Koki Muraoka,Yibo Wang,Junhan Chang,Fengbo Yuan,Sigbjørn Løland Bore,Chun Cai,Yen-Chih Lin,Bo Wang,Jiayan Xu,Jiaxin Zhu,Chenxing Luo,Yuzhi Zhang,Rhys E. A. Goodall,Wenshuo Liang,Anurag K. Singh,Sikai Yao,Jingchao Zhang,Renata M. Wentzcovitch,Jiequn Han,Jie Liu,Weile Jia,Darrin M. York,E Weinan,Roberto Car,Linfeng Zhang,Han Wang
摘要
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials 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, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.