{
  "abstract": "Describing density-of-states occupation with high precision in disordered systems is crucial for modeling charge transport and understanding doping mechanisms or interfacial energy-level alignment. Beyond fundamental insight from a mechanistic understanding of these physical phenomena, accurate modeling is pivotal for optimizing technological applications in optoelectronic and thermoelectric devices. However, the required calculations are computationally demanding and inherently complex, preventing systematic computational-experimental approaches. Here, we leverage neural networks to perform these calculations precisely and efficiently. We first demonstrate this approach for the Fermi-Dirac occupation of Gaussian densities of states, achieving accelerations of up to three orders of magnitude, a substantial advantage for advanced modeling workflows that require repeated evaluation of the underlying integrals. We then introduce a neural network for doped molecular materials, such as organic semiconductors, that directly predicts doping levels and Fermi level shifts, circumventing conventional recursive schemes and accelerating computations by nearly five orders of magnitude.",
  "abstract_shown": true,
  "affiliations": [
    "Centre Énergie Matériaux Télécommunications",
    "Department of Physics",
    "Concordia University",
    "Department of Chemistry and Biochemistry",
    "Centre for Research in Molecular Modeling (CERMM",
    "Centre for NanoScience Research (CeNSR"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Melissa Berteau-Rainville",
      "orcid": "0009-0000-0703-9205"
    },
    {
      "affs": [
        0
      ],
      "name": "Emanuele Orgiu"
    },
    {
      "affs": [
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Ingo Salzmann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e9r2pxvanr7yc.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009841/v1",
  "fulltext_url": null,
  "id": "e9r2pxvanr7yc",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009841/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220432776",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009841/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/e9r2pxvanr7yc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15009841",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009841/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Solving density-of-states occupation in disordered systems using neural networks",
  "title_text": "Solving density-of-states occupation in disordered systems using neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/e9r2pxvanr7yc",
  "venue": "ChemRxiv",
  "versions": []
}