{
  "abstract": "Kohn-Sham density functional theory (KS-DFT) has found widespread application in accurate electronic structure calculations. However, it can be computationally demanding especially for large-scale simulations, motivating recent efforts toward its machine-learning (ML) acceleration. We propose a neural network self-consistent fields (NeuralSCF) framework that establishes the Kohn-Sham density map as a deep learning objective, which encodes the mechanics of the Kohn-Sham equations. Modeling this map with an SE(3)-equivariant graph transformer, NeuralSCF emulates the Kohn-Sham self-consistent iterations to obtain electron densities, from which other properties can be derived. NeuralSCF achieves state-of-the-art accuracy in electron density prediction and derived properties, featuring exceptional zero-shot generalization to a remarkable range of out-of-distribution systems. NeuralSCF reveals that learning from KS-DFT’s intrinsic mechanics significantly enhances the model’s accuracy and transferability, offering a promising stepping stone for accelerating electronic structure calculations through mechanics learning.",
  "abstract_shown": true,
  "affiliations": [
    "International Center for Quantum Materials, School of Physics, Peking University, Beijing, China",
    "Beijing Key Laboratory of Quantum Devices, Peking University, Beijing, China",
    "Hefei National Laboratory, Hefei, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Feitong Song"
    },
    {
      "affs": [
        1,
        2,
        0
      ],
      "name": "J N Feng",
      "orcid": "0009-0001-4383-6719"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/br6hbf4p5f8wm.bib",
  "cited_by": 6,
  "doi": "10.1038/s41524-026-02110-0",
  "fulltext_url": null,
  "id": "br6hbf4p5f8wm",
  "issns": [
    "2057-3960"
  ],
  "landing_url": "https://doi.org/10.1038/s41524-026-02110-0",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7162860520",
  "pdf_url": "https://www.nature.com/articles/s41524-026-02110-0_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-30",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/br6hbf4p5f8wm.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:07Z",
      "native_id": "W7162860520",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41524-026-02110-0"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Neural network self-consistent fields for density functional theory",
  "title_text": "Neural network self-consistent fields for density functional theory",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:23Z",
  "url": "https://research.draconitic.com/paper/br6hbf4p5f8wm",
  "venue": "npj Computational Materials",
  "versions": []
}