{
  "abstract": "Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We introduce O-kRystal, a dual-space orbital-learning framework that uses a lower-cost Perdew-Burke-Ernzerhof (PBE) calculation as both an energy baseline and a source of structured orbital information. Combining PBE band energies with real- and reciprocal-space orbital matrices, O-kRystal predicts higher-level corrections for individual electronic states as a function of crystal momentum and spin. For states within 5 eV of the band-edge midpoint, it predicts Heyd-Scuseria-Ernzerhof (HSE06) band energies with a mean absolute error of $0.171 \\pm 0.009$ eV across nearly 6,000 three-dimensional materials spanning insulating, metallic, magnetic, and nonmagnetic systems. On a separate benchmark of six strongly correlated materials, it reduces the corresponding PBE band-energy MAE by 68.7% and resolves orbital-dependent changes in the relative placement of transition-metal d and ligand p states. Despite training only at high-symmetry points, it maintains comparable accuracy at unseen points along band paths and reconstructs HSE06 densities of states from dense Brillouin-zone predictions. Finally, training O-kRystal on $G_0W_0$ quasiparticle energies for two-dimensional materials demonstrates its applicability across levels of theory and the potential of orbital learning for higher-fidelity, momentum-resolved electronic-structure prediction beyond density functional theory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19919",
  "author_total": 7,
  "authors": [
    {
      "name": "Minhyuk Kang"
    },
    {
      "name": "Beom Seok Kang"
    },
    {
      "name": "Nikolai Argatoff"
    },
    {
      "name": "Miguel Nouman"
    },
    {
      "name": "Danish Khan"
    },
    {
      "name": "William A. Goddard III"
    },
    {
      "name": "Anima Anandkumar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7b9m5c59b94nw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19919",
  "fulltext_url": null,
  "id": "7b9m5c59b94nw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19919",
  "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.LG"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7222277270",
  "pdf_url": "https://arxiv.org/pdf/2610.19919",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7b9m5c59b94nw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.19919",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19919"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Beyond DFT electronic band structures via dual-space orbital learning",
  "title_text": "Beyond DFT electronic band structures via dual-space orbital learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/7b9m5c59b94nw",
  "venue": "arXiv",
  "versions": []
}