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DeePMD-kit v2: A software package for deep potential models.

Authors :
Zeng, Jinzhe
Zhang, Duo
Lu, Denghui
Mo, Pinghui
Li, Zeyu
Chen, Yixiao
Rynik, Marián
Huang, Li'ang
Li, Ziyao
Shi, Shaochen
Wang, Yingze
Ye, Haotian
Tuo, Ping
Yang, Jiabin
Ding, Ye
Li, Yifan
Tisi, Davide
Zeng, Qiyu
Bao, Han
Xia, Yu
Source :
Journal of Chemical Physics; 8/7/2023, Vol. 159 Issue 5, p1-24, 24p
Publication Year :
2023

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00219606
Volume :
159
Issue :
5
Database :
Complementary Index
Journal :
Journal of Chemical Physics
Publication Type :
Academic Journal
Accession number :
169805969
Full Text :
https://doi.org/10.1063/5.0155600