{
  "abstract": "The conventional distinction between semiconductors and insulators is often based on the magnitude of the band gap, with materials exhibiting gaps wider than 3 eV typically classified as insulators. However, the emergence of ultra-wide-band-gap (UWBG) semiconductors such as AlGaN, diamond, BN, and β-Ga 2 O 3 challenges this paradigm for materials classification and raises fundamental questions about the upper bound of band gaps compatible with semiconducting behavior. Here we develop a computational-discovery strategy to identify semiconductors with band gaps exceeding that of AlN (6.2 eV), while retaining essential semiconducting properties such as shallow dopants and mobile charge carriers. Consistent with prior observations on individual materials, we find more generally that materials composed of light elements in densely packed crystal structures exhibit wide band gaps and light carrier effective masses that enable shallow dopants, high mobility, and weak polaron binding. By applying the hydrogenic Bohr model and first-principles defect calculations – validated against available experimental data – to screen for materials with shallow dopants, we identify dopable compounds with gaps as wide as 9.5 eV that nonetheless host mobile charge carriers. Our findings demonstrate that semiconducting behavior persists even at extreme band gaps, far beyond conventional upper bounds traditionally associated with semiconductor materials.",
  "abstract_shown": true,
  "affiliations": [
    "Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA",
    "School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA",
    "Department of Physics, The University of Texas at Austin, Austin, TX, USA",
    "Department of Physics, University of the Basque Country UPV/EHU, Leioa, Spain",
    "Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Sieun Chae",
      "orcid": "0000-0002-9489-9023"
    },
    {
      "affs": [
        0
      ],
      "name": "Nocona Sanders",
      "orcid": "0000-0003-2471-2128"
    },
    {
      "affs": [
        0
      ],
      "name": "Kelsey Mengle",
      "orcid": "0000-0001-6812-8113"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Wang",
      "orcid": "0000-0002-5084-4039"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoli Zhang",
      "orcid": "0000-0002-4226-7622"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Jon Lafuente-Bartolome"
    },
    {
      "affs": [
        2,
        4
      ],
      "name": "Kaifa Luo",
      "orcid": "0000-0003-2572-020X"
    },
    {
      "affs": [
        1
      ],
      "name": "Yen‐Chun Huang",
      "orcid": "0000-0001-9652-0737"
    },
    {
      "affs": [
        2,
        4
      ],
      "name": "Feliciano Giustino",
      "orcid": "0000-0001-9293-1176"
    },
    {
      "affs": [
        0
      ],
      "name": "John T. Heron",
      "orcid": "0000-0001-9632-9502"
    },
    {
      "affs": [
        0
      ],
      "name": "Emmanouil Kioupakis",
      "orcid": "0000-0003-1880-6443"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/99grss7y04s3z.bib",
  "cited_by": 5,
  "doi": "10.1038/s41524-026-02173-z",
  "fulltext_url": null,
  "id": "99grss7y04s3z",
  "issns": [
    "2057-3960"
  ],
  "landing_url": "https://doi.org/10.1038/s41524-026-02173-z",
  "language": "en",
  "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": "W4417128733",
  "pdf_url": "https://www.nature.com/articles/s41524-026-02173-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-21",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/99grss7y04s3z.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:57Z",
      "native_id": "W4417128733",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41524-026-02173-z"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Extreme-band-gap semiconductors with shallow dopants and mobile carriers",
  "title_text": "Extreme-band-gap semiconductors with shallow dopants and mobile carriers",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:23Z",
  "url": "https://research.draconitic.com/paper/99grss7y04s3z",
  "venue": "npj Computational Materials",
  "versions": []
}