{
  "abstract": "The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system, such that all GS observables can be expressed as functionals of the GS charge density. Conventional Kohn-Sham density functional theory requires iterative solution of the self-consistent-field equations at substantial computational cost, motivating the development of deep learning surrogates for electronic structure calculations and, in turn, accelerating computer-aided materials design. Here, we propose AIDEN, an Atomic-Interaction Density Equivariant Network for solving real-space charge density. AIDEN separates the element-dependent one-center density from environment-induced density redistribution and represents the latter through complementary atom- and edge-centered tensor correlations. A continuous low-rank Gaussian decoder then reconstructs the density at arbitrary spatial coordinates while reusing atomic encodings independently of the evaluation grid. AIDEN achieves state-of-the-art accuracy on periodic crystal benchmarks while remaining competitive for molecular systems, and further demonstrates zero-shot transferability across several structurally distinct out-of-distribution case studies. Furthermore, AIDEN provides substantially faster inference than both baseline models and full SCF calculations, enabling efficient charge density reconstruction for large-scale electronic structure calculations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.14906",
  "author_total": 4,
  "authors": [
    {
      "name": "Yuxuan Zeng"
    },
    {
      "name": "Tong Xie"
    },
    {
      "name": "Taoyuze Lv"
    },
    {
      "name": "Zhicheng Zhong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dhkcn9m7xg6ym.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.14906",
  "fulltext_url": null,
  "id": "dhkcn9m7xg6ym",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.14906",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7213225771",
  "pdf_url": "https://arxiv.org/pdf/2609.14906",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dhkcn9m7xg6ym.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2609.14906",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.14906"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Neural-Network Solutions to Real-Space Charge Density and Generalization",
  "title_text": "Neural-Network Solutions to Real-Space Charge Density and Generalization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/dhkcn9m7xg6ym",
  "venue": "arXiv",
  "versions": []
}