GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Ke Xu, H. Bu, Shuning Pan, Eric Lindgren, Yong-Chao Wu, Yong Wang, Jiahui Liu, Keke Song, Bin Xu, Yifan Li, Tobias Hainer, L.A. Svensson, Julia Wiktor, Rui Zhao, Hongfu Huang, Cheng Qian, Shuo Zhang, Zezhu Zeng, Bohan Zhang, Benrui Tang, Yang Xiao, Zihan Yan, Jiuyang Shi, Zhixin Liang, Junjie Wang, Ting Liang, Shuo Cao, Yanzhou Wang, Penghua Ying, Nan Xu, Chengbing Chen, Yuwen Zhang, Zherui Chen, Xin Wu, Wenwu Jiang, Esmée Berger, Yanlong Li, Shunda Chen, Alexander J. Gabourie, Haikuan Dong, Shiyun Xiong, Ning Wei, Yue Chen, Jianbin Xu, Feng Ding, Zhimei Sun, Tapio Ala-Nissilä, Ari Harju, Jin‐Cheng Zheng, Pengfei Guan, Paul Erhart, Jian Sun, Wengen Ouyang, Yanjing Su, Zheyong Fan
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.