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Peer-reviewedCC BYMaterials Genome Engineering Advances

GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP

  • Zihan Yan1,2
  • Denan Li3
  • Xin Wu4
  • Zhoulin Liu5
  • Chen Hua6,7
  • Boyi Situ1,2
  • Hao Yang8
  • Shengjie Tang1,2
  • Benrui Tang9
  • Ziyang Wang10,11
  • Shangzhao Yi12
  • Hao Wang13
11 more authors
  • Dian Huang14
  • Ke Li15
  • Qilin Guo16
  • Zherui Chen17
  • Ke Xu9
  • Yanzhou Wang18
  • Zi Wang10,11
  • Gang Tang19
  • Shi Liu3
  • Zheyong Fan9
  • Yizhou Zhu1
19 affiliations
  1. 1Department of Materials Science and Engineering School of Engineering Westlake University Hangzhou Zhejiang China
  2. 2School of Materials Science and Engineering Zhejiang University Hangzhou Zhejiang China
  3. 3Department of Physics School of Science Westlake University Hangzhou Zhejiang China
  4. 4Institute of Industrial Science The University of Tokyo Tokyo Japan
  5. 5School of Science Harbin Institute of Technology Shenzhen Guangdong China
  6. 6School of Future Technology University of Chinese Academy of Sciences Beijing China
  7. 7State Key Lab of Cryogenic Science and Technology Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing China
  8. 8School of Physical Science and Technology ShanghaiTech University Shanghai China
  9. 9College of Physical Science and Technology Bohai University Jinzhou China
  10. 10National Engineering Laboratory for Reducing Emissions from Coal Combustion Shandong Key Laboratory of Green Thermal Power and Carbon Reduction Shandong University Jinan Shandong China
  11. 11School of Energy and Power Engineering Key Lab of Ocean Energy Utilization and Conservation of Ministry Education Dalian University of Technology Dalian China
  12. 12Suzhou Institute of Nano‐Tech and Nano‐Bionics Chinese Academy of Sciences Suzhou China
  13. 13School of Materials Science and Engineering Wuhan University of Technology Wuhan China
  14. 14MOE Key Laboratory of Thermo‐Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Xi'an China
  15. 15Department of Thermal Science and Energy Engineering School of Engineering Science University of Science and Technology of China Hefei Anhui China
  16. 16Hangzhou International Innovation Institute Beihang University Hangzhou China
  17. 17Future Technology School Shenzhen Technology University Shenzhen China
  18. 18School of Electronic Engineering Chengdu Technological University Chengdu China
  19. 19School of Interdisciplinary Science Beijing Institute of Technology Beijing China
Open at sourceDOICite

Abstract

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

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Cite

BibTeXRIS
BibTeX
@article{yan2026gpumdkit,
  title = {{GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP}},
  author = {Zihan Yan and Denan Li and Xin Wu and Zhoulin Liu and Chen Hua and Boyi Situ and Hao Yang and Shengjie Tang and Benrui Tang and Ziyang Wang and Shangzhao Yi and Hao Wang and Dian Huang and Ke Li and Qilin Guo and Zherui Chen and Ke Xu and Yanzhou Wang and Zi Wang and Gang Tang and Shi Liu and Zheyong Fan and Yizhou Zhu},
  journal = {Materials Genome Engineering Advances},
  year = {2026},
  month = jun,
  doi = {10.1002/mgea.70074},
  url = {https://doi.org/10.1002/mgea.70074},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP
AU  - Zihan Yan
AU  - Denan Li
AU  - Xin Wu
AU  - Zhoulin Liu
AU  - Chen Hua
AU  - Boyi Situ
AU  - Hao Yang
AU  - Shengjie Tang
AU  - Benrui Tang
AU  - Ziyang Wang
AU  - Shangzhao Yi
AU  - Hao Wang
AU  - Dian Huang
AU  - Ke Li
AU  - Qilin Guo
AU  - Zherui Chen
AU  - Ke Xu
AU  - Yanzhou Wang
AU  - Zi Wang
AU  - Gang Tang
AU  - Shi Liu
AU  - Zheyong Fan
AU  - Yizhou Zhu
PY  - 2026
DA  - 2026/06/01
JO  - Materials Genome Engineering Advances
DO  - 10.1002/mgea.70074
UR  - https://doi.org/10.1002/mgea.70074
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
SN  - 2940-9489
SN  - 2940-9497
ER  -