{
  "abstract": "In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physics, Tsinghua University, Beijing, Beijing, China",
    "State Key Laboratory of Low Dimensional Quantum Physics, Tsinghua University, Beijing, Beijing, China",
    "Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA",
    "Institute for Advanced Study, Tsinghua University, Beijing, Beijing, China",
    "Department of Physics, University of California, Berkeley, CA, USA",
    "Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA",
    "Suzhou National Laboratory, Suzhou, Jiangsu, China",
    "Frontier Science Center for Quantum Information, Tsinghua University, Beijing, Beijing, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 18,
  "authors": [
    {
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        0,
        1
      ],
      "name": "Li Yang",
      "orcid": "0000-0002-8611-6359"
    },
    {
      "affs": [
        2,
        0,
        3,
        1
      ],
      "name": "Yanzhen Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Boheng Zhao",
      "orcid": "0009-0005-5267-8459"
    },
    {
      "affs": [
        0,
        4,
        5
      ],
      "name": "Xiaoxun Gong",
      "orcid": "0000-0002-6459-4288"
    },
    {
      "affs": [
        2,
        0,
        3,
        1
      ],
      "name": "Yuxiang Wang",
      "orcid": "0000-0003-3947-9912"
    },
    {
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        0,
        1
      ],
      "name": "Zechen Tang",
      "orcid": "0009-0003-1916-5622"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zixu Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zilong Yuan"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jialin Li"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Minghui Sun"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zezhou Chen",
      "orcid": "0009-0001-4739-5789"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Honggeng Tao",
      "orcid": "0000-0001-7580-9275"
    },
    {
      "affs": [
        0,
        1,
        6
      ],
      "name": "Baochun Wu",
      "orcid": "0000-0001-6661-4804"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yuhang Yu"
    },
    {
      "affs": [
        0,
        3,
        1
      ],
      "name": "He Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Felipe H. da Jornada",
      "orcid": "0000-0001-6712-7151"
    },
    {
      "affs": [
        0,
        7,
        3,
        1,
        6
      ],
      "name": "Wenhui Duan",
      "orcid": "0000-0001-9685-2547"
    },
    {
      "affs": [
        0,
        7,
        1,
        6
      ],
      "name": "Yong Xu",
      "orcid": "0000-0002-4844-2460"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f638wx09zq47k.bib",
  "cited_by": 7,
  "doi": "10.1038/s41524-026-02219-2",
  "fulltext_url": null,
  "id": "f638wx09zq47k",
  "issns": [
    "2057-3960"
  ],
  "landing_url": "https://doi.org/10.1038/s41524-026-02219-2",
  "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": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
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  "pdf_url": "https://www.nature.com/articles/s41524-026-02219-2_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-09",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/f638wx09zq47k.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:07Z",
      "native_id": "W7167797316",
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      "url": "https://doi.org/10.1038/s41524-026-02219-2"
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "DeepH-pack: a general-purpose neural network package for deep-learning electronic structure calculations",
  "title_text": "DeepH-pack: a general-purpose neural network package for deep-learning electronic structure calculations",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:23Z",
  "url": "https://research.draconitic.com/paper/f638wx09zq47k",
  "venue": "npj Computational Materials",
  "versions": []
}