{
  "abstract": "Strong electron correlation governs the chemistry of bond breaking, polyradicals, and excited states, yet accurately describing it requires computational methods whose cost increases steeply with system size. We present a scalable ensemble density functional theory (eDFT) methodology that extends accurate treatment of strong correlation to molecular systems of realistic size at standard Kohn-Sham DFT cost. Automated derivation of the method’s working equations and a data-driven acceleration scheme enable linear scaling with active-space size while retaining multiconfigurational accuracy. Applied to systems inaccessible to standard Kohn-Sham DFT methods, such as multiple bond dissociation, open-shell polyradicals, and a recently synthesized magnetic quantum material, the framework reproduces experimental magnetic gaps and near-exact wavefunction benchmarks using active spaces up to ten electrons in ten orbitals, thus removing a long-standing barrier to routine simulation of strongly correlated matter.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST, UNIST-gil 50, Ulsan, Republic of Korea, 44919",
    "Center for Multidimensional Carbon Materials, Institute for Basic Science (IBS, UNIST-gil 50, Ulsan, Republic of Korea, 44919"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Konstantin Komarov"
    },
    {
      "affs": [
        1
      ],
      "name": "Michael Filatov(Gulak)"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Seung Kyu Min"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7epf1f5vnm1d3.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008475/v1",
  "fulltext_url": null,
  "id": "7epf1f5vnm1d3",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008475/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7211954301",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/7epf1f5vnm1d3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008475",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008475/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Automated Symbolic Derivation Enables Scalable Ensemble DFT for Strongly Correlated Molecules and Materials",
  "title_text": "Automated Symbolic Derivation Enables Scalable Ensemble DFT for Strongly Correlated Molecules and Materials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/7epf1f5vnm1d3",
  "venue": "ChemRxiv",
  "versions": []
}