{
  "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",
  "abstract_shown": true,
  "affiliations": [
    "Department of Materials Science and Engineering School of Engineering Westlake University Hangzhou Zhejiang China",
    "School of Materials Science and Engineering Zhejiang University Hangzhou Zhejiang China",
    "Department of Physics School of Science Westlake University Hangzhou Zhejiang China",
    "Institute of Industrial Science The University of Tokyo Tokyo Japan",
    "School of Science Harbin Institute of Technology Shenzhen Guangdong China",
    "School of Future Technology University of Chinese Academy of Sciences Beijing China",
    "State Key Lab of Cryogenic Science and Technology Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing China",
    "School of Physical Science and Technology ShanghaiTech University Shanghai China",
    "College of Physical Science and Technology Bohai University Jinzhou China",
    "National Engineering Laboratory for Reducing Emissions from Coal Combustion Shandong Key Laboratory of Green Thermal Power and Carbon Reduction Shandong University Jinan Shandong China",
    "School of Energy and Power Engineering Key Lab of Ocean Energy Utilization and Conservation of Ministry Education Dalian University of Technology Dalian China",
    "Suzhou Institute of Nano‐Tech and Nano‐Bionics Chinese Academy of Sciences Suzhou China",
    "School of Materials Science and Engineering Wuhan University of Technology Wuhan China",
    "MOE Key Laboratory of Thermo‐Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Xi'an China",
    "Department of Thermal Science and Energy Engineering School of Engineering Science University of Science and Technology of China Hefei Anhui China",
    "Hangzhou International Innovation Institute Beihang University Hangzhou China",
    "Future Technology School Shenzhen Technology University Shenzhen China",
    "School of Electronic Engineering Chengdu Technological University Chengdu China",
    "School of Interdisciplinary Science Beijing Institute of Technology Beijing China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 23,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Zihan Yan",
      "orcid": "0000-0002-8911-6549"
    },
    {
      "affs": [
        2
      ],
      "name": "Denan Li"
    },
    {
      "affs": [
        3
      ],
      "name": "Xin Wu",
      "orcid": "0000-0002-7179-371X"
    },
    {
      "affs": [
        4
      ],
      "name": "Zhoulin Liu",
      "orcid": "0000-0001-8755-082X"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Chen Hua",
      "orcid": "0009-0008-8502-1068"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Boyi Situ",
      "orcid": "0000-0002-2275-7165"
    },
    {
      "affs": [
        7
      ],
      "name": "Hao Yang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Shengjie Tang",
      "orcid": "0000-0001-8036-5189"
    },
    {
      "affs": [
        8
      ],
      "name": "Benrui Tang",
      "orcid": "0009-0003-0377-7798"
    },
    {
      "affs": [
        9,
        10
      ],
      "name": "Ziyang Wang"
    },
    {
      "affs": [
        11
      ],
      "name": "Shangzhao Yi"
    },
    {
      "affs": [
        12
      ],
      "name": "Hao Wang",
      "orcid": "0009-0002-8735-8373"
    },
    {
      "affs": [
        13
      ],
      "name": "Dian Huang"
    },
    {
      "affs": [
        14
      ],
      "name": "Ke Li"
    },
    {
      "affs": [
        15
      ],
      "name": "Qilin Guo"
    },
    {
      "affs": [
        16
      ],
      "name": "Zherui Chen",
      "orcid": "0000-0003-1318-1907"
    },
    {
      "affs": [
        8
      ],
      "name": "Ke Xu"
    },
    {
      "affs": [
        17
      ],
      "name": "Yanzhou Wang",
      "orcid": "0000-0003-1509-4364"
    },
    {
      "affs": [
        9,
        10
      ],
      "name": "Zi Wang",
      "orcid": "0000-0002-3953-0493"
    },
    {
      "affs": [
        18
      ],
      "name": "Gang Tang",
      "orcid": "0000-0003-4665-8599"
    },
    {
      "affs": [
        2
      ],
      "name": "Shi Liu",
      "orcid": "0000-0002-8488-4848"
    },
    {
      "affs": [
        8
      ],
      "name": "Zheyong Fan"
    },
    {
      "affs": [
        0
      ],
      "name": "Yizhou Zhu",
      "orcid": "0000-0002-5819-7657"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/60t9t48nve61v.bib",
  "cited_by": 5,
  "doi": "10.1002/mgea.70074",
  "fulltext_url": null,
  "id": "60t9t48nve61v",
  "issns": [
    "2940-9489",
    "2940-9497"
  ],
  "landing_url": "https://doi.org/10.1002/mgea.70074",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2505|Machine Learning in Materials Science"
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  "openalex": "W7163573401",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-01",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/60t9t48nve61v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:27:01Z",
      "native_id": "W7163573401",
      "source": "openalex",
      "url": "https://doi.org/10.1002/mgea.70074"
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP",
  "title_text": "GPUMDkit: A User‐Friendly Toolkit for GPUMD and NEP",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:45Z",
  "url": "https://research.draconitic.com/paper/60t9t48nve61v",
  "venue": "Materials Genome Engineering Advances",
  "versions": []
}