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

JZ Zeng and D Zhang and DH Lu and PH Mo and ZY Li and YX Chen and M Rynik and LA Huang and ZY Li and SC Shi and YZ Wang and HT Ye and P Tuo and JB Yang and Y Ding and YF Li and D Tisi and QY Zeng and H Bao and Y Xia and JM Huang and K Muraoka and YB Wang and JH Chang and FB Yuan and SL Bore and C Cai and YN Lin and B Wang and JY Xu and JX Zhu and CX Luo and YZ Zhang and REA Goodall and WS Liang and AK Singh and SK Yao and JC Zhang and R Wentzcovitch and JQ Han and J Liu and WL Jia and DM York and WA E and R Car and LF Zhang and H Wang, JOURNAL OF CHEMICAL PHYSICS, 159, 054801 (2023).

DOI: 10.1063/5.0155600

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.

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