TY - JOUR
T1 - GPUMDkit
T2 - A User-Friendly Toolkit for GPUMD and NEP
AU - Yan, Zihan
AU - Li, Denan
AU - Wu, Xin
AU - Liu, Zhoulin
AU - Hua, Chen
AU - Situ, Boyi
AU - Yang, Hao
AU - Tang, Shengjie
AU - Tang, Benrui
AU - Wang, Ziyang
AU - Yi, Shangzhao
AU - Wang, Huan
AU - Huang, Dian
AU - Li, Ke
AU - Guo, Qilin
AU - Chen, Zherui
AU - Xu, Ke
AU - Wang, Yanzhou
AU - Wang, Ziliang
AU - Tang, Gang
AU - Liu, Shi
AU - Fan, Zheyong
AU - Zhu, Yizhou
N1 - Publisher Copyright:
© 2026 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.
PY - 2026/6
Y1 - 2026/6
N2 - Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum–mechanical accuracy at empirical–potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications. Trial Registration: GitHub Repository: https://github.com/zhyan0603/GPUMDkit.
AB - Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum–mechanical accuracy at empirical–potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications. Trial Registration: GitHub Repository: https://github.com/zhyan0603/GPUMDkit.
UR - https://www.scopus.com/pages/publications/105048965222
U2 - 10.1002/mgea.70074
DO - 10.1002/mgea.70074
M3 - Article
AN - SCOPUS:105048965222
SN - 2940-9489
VL - 4
JO - Materials Genome Engineering Advances
JF - Materials Genome Engineering Advances
IS - 2
M1 - e70074
ER -