GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials

Abstract This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics (GPUMD) package, GPUMD 4.0. We begin with a brief review of its development history, starting from the initial version. We then discuss the theoretical foundations for the development of the GPUMD package, including the formulations of the interatomic force, virial and heat current for many‐body potentials, the development of the highly efficient and flexible neuroevolution potential (NEP) method, the supported integrators and related operations, the various physical properties that can be calculated on the fly, and the GPUMD ecosystem. After presenting these functionalities, we review a range of applications enabled by GPUMD, particularly in combination with the NEP approach. Finally, we outline possible future development directions for GPUMD.

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Publication Details

Journal
Materials Genome Engineering Advances
Published
2025-08-03
DOI
https://doi.org/10.1002/mgea.70028
Citations
46
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
23.82
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GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials

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46 citations
Materials Genome Engineering Advances
Machine Learning in Materials Science
23.82
article

GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials

Xin Wu, Ari Harju, Yanlong Li, Eric Lindgren, Alexander J. Gabourie, Zheyong Fan, Yong-Chao Wu, Ting Liang, Shiyun Xiong, Haikuan Dong, Zihan Yan, Jianbin Xu, Esmée Berger, Keke Song, Zherui Chen, Wengen Ouyang, Hongfu Huang, Zhixin Liang, Zhimei Sun, Yanjing Su, Paul Erhart, Ke Wei Xu, Jin‐Cheng Zheng, Bohan Zhang, Bing Tang, Penghua Ying, Julia Wiktor, Shunda Chen, Tapio Ala-Nissilä, Chengbing Chen, Shuning Pan, Jiahui Liu, Junjie Wang, Yong Wang, Yifan Li, Shuo Zhang, Yue Chen, Jian Sun, Bin Xu, Feng Ding, Rui Zhao, Yang Xiao, Nan Xu, Shuo Cao, Zezhu Zeng, Pengfei Guan, Cheng Qian, Wenwu Jiang, L.A. Svensson, Ning Wei, Jiuyang Shi, Hangyu Bu, Yuwen Zhang, Yanzhou Wang, Tobias Hainer
article en
46 citations

Abstract

Abstract This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics (GPUMD) package, GPUMD 4.0. We begin with a brief review of its development history, starting from the initial version. We then discuss the theoretical foundations for the development of the GPUMD package, including the formulations of the interatomic force, virial and heat current for many‐body potentials, the development of the highly efficient and flexible neuroevolution potential (NEP) method, the supported integrators and related operations, the various physical properties that can be calculated on the fly, and the GPUMD ecosystem. After presenting these functionalities, we review a range of applications enabled by GPUMD, particularly in combination with the NEP approach. Finally, we outline possible future development directions for GPUMD.

Materials Genome Engineering AdvancesVol. 3(3)
Jiangnan University (CN), Beijing Institute of Technology (CN), Guangdong University of Technology (CN), Loughborough University (GB), Qilu University of Technology (CN), Tel Aviv University (IL), Chinese University of Hong Kong (HK), Shenzhen University (CN), Xiamen University (CN), George Washington University (US), Wuhan University of Technology (CN), Princeton University (US), Westlake University (CN), Wuhan University (CN), Guilin University of Technology (CN), Xinyu University (CN), Deep Space Industries (United States) (US), Xiamen University Malaysia (MY), Collaborative Innovation Center of Advanced Microstructures (CN), Shandong Academy of Sciences (CN), Shenzhen Technology University (CN), Ningbo Institute of Industrial Technology (CN), Eastern Institute of Technology, Ningbo, Chalmers University of Technology (SE), The University of Tokyo (JP), Zhejiang University (CN), Beihang University (CN), Nanjing University (CN), University of Hong Kong (HK), South China University of Technology (CN), University of Science and Technology Beijing (CN), Bohai University (CN), Aalto University (FI)
Openalex Percentile: Top 0%
Machine Learning in Materials Science
23.82
DOIOpen Access PDFOpenAlex
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