GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910057766060032 |
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| author | Yan, Zihan Li, Denan Wu, Xin Liu, Zhoulin Hua, Chen Situ, Boyi Yang, Hao Tang, Shengjie Tang, Benrui Wang, Ziyang Yi, Shangzhao Wang, Huan Huang, Dian Li, Ke Guo, Qilin Chen, Zherui Xu, Ke Wang, Yanzhou Wang, Ziliang Tang, Gang Liu, Shi Fan, Zheyong Zhu, Yizhou |
| author_facet | Yan, Zihan Li, Denan Wu, Xin Liu, Zhoulin Hua, Chen Situ, Boyi Yang, Hao Tang, Shengjie Tang, Benrui Wang, Ziyang Yi, Shangzhao Wang, Huan Huang, Dian Li, Ke Guo, Qilin Chen, Zherui Xu, Ke Wang, Yanzhou Wang, Ziliang Tang, Gang Liu, Shi Fan, Zheyong Zhu, Yizhou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17367 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP Yan, Zihan Li, Denan Wu, Xin Liu, Zhoulin Hua, Chen Situ, Boyi Yang, Hao Tang, Shengjie Tang, Benrui Wang, Ziyang Yi, Shangzhao Wang, Huan Huang, Dian Li, Ke Guo, Qilin Chen, Zherui Xu, Ke Wang, Yanzhou Wang, Ziliang Tang, Gang Liu, Shi Fan, Zheyong Zhu, Yizhou Materials Science 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. |
| title | GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.17367 |