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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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作者 Ke Xu Hekai Bu +52 位作者 Shuning Pan Eric Lindgren Yongchao Wu Yong Wang Jiahui Liu Keke Song Bin Xu Yifan Li Tobias Hainer Lucas 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 Esme Berger Yanlong Li Shunda chen Alexander JGabourie Haikuan Dong Shiyun Xiong Ning Wei Yue chen Jianbin Xu Feng Ding Zhimei Sun Tapio Ala-Nissila Ari Harju Jincheng Zheng Pengfei Guan Paul Erhart Jian Sun Wengen Ouyang Yanjing Su Zheyong Fan 《Materials Genome Engineering Advances》 2025年第3期1-24,共24页
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 fr... 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. 展开更多
关键词 GPUMD interatomic potential machine-learned potential materials simulation molecular dynamics
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