{
  "abstract": "Accurate quasiparticle electronic structures are the foundation for understanding excited-state properties of materials and explaining optoelectronic, quantum, and transport phenomena. First-principles GW calculations nevertheless remain computationally intensive for large or configurationally complex systems. Here we introduce DeepH-GW, a deep-learning framework that predicts an effective GW quasiparticle Hamiltonian directly from atomic structure. Building on the local, equivariant message-passing architecture of DeepH, DeepH-GW is trained on high-fidelity plane-wave GW calculations through a real-space Hamiltonian-reconstruction interface. This approach combines the systematic accuracy and broad chemical applicability of plane-wave methods with linear-scaling neural-network inference. Across the systems examined, DeepH-GW reproduces quasiparticle band structures with errors on the order of a few meV. Moreover, we show that, despite the intrinsic nonlocality of many-body interactions, DeepH-GW exhibits strong cross-scale transferability from relatively small training structures to substantially larger supercells. We demonstrate that the framework can accurately capture GW-level electron-phonon band-gap renormalization through predictions in supercells with thermal displacements, illustrating the power of the approach. DeepH-GW therefore provides a practical route toward large-scale many-body simulations and foundation models for excited-state electronic structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36962",
  "author_total": 7,
  "authors": [
    {
      "name": "Xiaoxun Gong"
    },
    {
      "name": "Zechen Tang"
    },
    {
      "name": "Woochang Kim"
    },
    {
      "name": "Yang Li"
    },
    {
      "name": "Wenhui Duan"
    },
    {
      "name": "Yong Xu"
    },
    {
      "name": "Steven G. Louie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dz67ds6c342x4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36962",
  "fulltext_url": null,
  "id": "dz67ds6c342x4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36962",
  "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.mes-hall"
    ],
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214974137",
  "pdf_url": "https://arxiv.org/pdf/2609.36962",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dz67ds6c342x4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36962",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36962"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mes-hall",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Deep Learning GW Quasiparticle Hamiltonians for Many-Body Excited-State Electronic Structure at Scale",
  "title_text": "Deep Learning GW Quasiparticle Hamiltonians for Many-Body Excited-State Electronic Structure at Scale",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/dz67ds6c342x4",
  "venue": "arXiv",
  "versions": []
}