{
  "abstract": "The electronic self-energy determines the single-particle Green's function and, through it, quasiparticle energies and spectral functions, but computing it from first principles is expensive and it is a difficult machine-learning target: complex-valued, matrix-valued in a local-orbital basis, and dependent on a continuous frequency. We predict the dynamical correlation part of the imaginary-frequency G_0W_0 self-energy matrix of molecules from atomic species and coordinates with an E(3)-equivariant message-passing network, the rotational covariance of the orbital-space matrix being imposed by the architecture itself and the static exchange part taken from the density-functional reference. Frequency enters either as an explicit network input or through frequency-specific output heads, and the predicted matrices are continued to the real axis in a representation in which causality and a non-negative spectral density hold by construction. The frequency-conditioned model reproduces matrix elements to below 1 meV on the imaginary axis for the systems studied here, and that accuracy survives the continuation: for water clusters, including a tetramer absent from the training set, and for chemically diverse molecules from QM9, the reconstructed spectral functions follow the G_0W_0 reference peak by peak and the frontier quasiparticle levels are recovered to a few tens of meV. Learning the self-energy on the imaginary axis is therefore a practical route to real-frequency spectra: once the model is trained, and given the density-functional reference it is built on, a new geometry costs no explicit G_0W_0 calculation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23717",
  "author_total": 6,
  "authors": [
    {
      "name": "Wudong Shen"
    },
    {
      "name": "Yang Zhong"
    },
    {
      "name": "Min-Ye Zhang"
    },
    {
      "name": "Xinyang Dong"
    },
    {
      "name": "Hongjun Xiang"
    },
    {
      "name": "Xinguo Ren"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dex9f03pzk71y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23717",
  "fulltext_url": null,
  "id": "dex9f03pzk71y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23717",
  "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"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7222915118",
  "pdf_url": "https://arxiv.org/pdf/2610.23717",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dex9f03pzk71y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.23717",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23717"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Learning the self-energy through equivariant neural networks",
  "title_text": "Learning the self-energy through equivariant neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/dex9f03pzk71y",
  "venue": "arXiv",
  "versions": []
}