{
  "abstract": "In OLED displays, phosphorescent emitter molecules are used for red and green but not for blue pixels. Iridium organometallics are promising phosphorescent emitters, but a molecule satisfying the requirements of color, brightness, and stability has not yet been developed. One strategy to discover an emitter is virtual screening, in which computational predictions are used to prioritize candidates for synthesis. Due to the computational difficulty of screening with first-principles methods such as density functional theory (DFT), empirical machine-learning methods have been used for virtual screening. Recently, machine-learned interatomic potentials (MLIPs) have provided fast proxies for DFT energies. This suggests that virtual screening could be conducted with ML proxies for DFT calculations rather than empirical ML, but this has not yet been applied to OLEDs. We show on a dataset of experimental values extracted from the literature that for emission wavelength, MLIP-based predictions are not inferior to empirical ML. Using an MLIP, we screened millions of molecules for blue emission. Although blue emission is not sufficient for an emitter, our results suggest that further development of virtual screening could focus on machine learned proxies for DFT prediction pipelines rather than models trained to experimental data.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physical and Environmental Sciences",
    "University of Toronto Scarborough",
    "Alliance for AI-Accelerated Materials Discovery",
    "Mila–Quebec AI Institute",
    "Department of Electrical and Computer Engineering",
    "University of Toronto",
    "Organic Semiconductor Centre, EaStCHEM School of Chemistry",
    "University of St Andrews",
    "Johns Hopkins University Scientific Software Engineering Center",
    "McCormick School of Engineering and Applied Science",
    "Northwestern University",
    "Paula M. Trienens Institute for Sustainability and Energy",
    "Department of Chemistry"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 17,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Alexander Davis",
      "orcid": "0000-0002-1797-2303"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Ihor Neporozhnii"
    },
    {
      "affs": [
        3
      ],
      "name": "Félix Therrien"
    },
    {
      "affs": [
        2,
        4,
        5
      ],
      "name": "Suhas Mahesh"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhibo Wang"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Ze-Lin Zhu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jeffrey Cheung"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Oleksandra Ostapenko"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Alex Zhuang"
    },
    {
      "affs": [
        8
      ],
      "name": "Xiang Chen"
    },
    {
      "affs": [
        8
      ],
      "name": "Arik Mitschang"
    },
    {
      "affs": [
        8
      ],
      "name": "Edward Hunter"
    },
    {
      "affs": [
        2
      ],
      "name": "Brandon Sutherland"
    },
    {
      "affs": [
        2,
        4,
        5
      ],
      "name": "Sjoerd Hoogland"
    },
    {
      "affs": [
        2,
        9,
        10,
        11
      ],
      "name": "Edward Sargent"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Eli Zysman-Colman"
    },
    {
      "affs": [
        0,
        1,
        2,
        12,
        5
      ],
      "name": "Oleksandr Voznyy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f0f80615kg45f.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15003869/v2",
  "fulltext_url": null,
  "id": "f0f80615kg45f",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003869/v2",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220836918",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15003869/v2",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/f0f80615kg45f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15003869",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003869/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Accelerating discovery of phosphorescent blue OLED emitters using chemical foundation models",
  "title_text": "Accelerating discovery of phosphorescent blue OLED emitters using chemical foundation models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/f0f80615kg45f",
  "venue": "ChemRxiv",
  "versions": []
}