{
  "abstract": "The accurate prediction of the bandgap ( $$\\text {E}_{\\text {g}}$$ ) and color of ceramic pigments is of considerable importance in materials science research. In this work, we investigate the $$\\text {E}_{\\text {g}}$$ and color of Cr $$_x$$ Sb $$_x$$ Ti $$_{1-2x}$$ O $$_2$$ rutile pigments by combining experimental, density functional theory (DFT and DFT+ U ) calculations, and neural network approaches. The main novelty of this study is the application of classical/hybrid quantum–classical neural networks to the prediction of pigment color and $$\\text {E}_{\\text {g}}$$ . Cr/Sb co-doped rutile pigment was synthesized and characterized, and the electronic structures of undoped and doped $${\\textrm{TiO}}_{2}$$ were analyzed using first-principles calculations. In parallel, classical and hybrid quantum–classical neural network models were developed and trained on datasets compiled from the Materials Project database and literature-reported rutile-based pigments. The models successfully captured the trends in $$\\text {E}_{\\text {g}}$$ and chromaticity, with the hybrid architectures providing slightly improved predictive performance and more consistent agreement with experiment. Overall, this work demonstrates an effective framework that links experiment, first-principles calculations, and classical/quantum–classical neural networks for predicting key properties of ceramic pigments, and it points toward the future use of fully quantum machine learning models for materials design.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "S. Y. Vaselnia"
    },
    {
      "name": "M. Khajeh Aminian",
      "orcid": "0000-0003-2396-8092"
    },
    {
      "name": "R. Dehghan Banadaki"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8t7hpp95vhejj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73340-x",
  "fulltext_url": null,
  "id": "8t7hpp95vhejj",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73340-x",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7215037802",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73340-x_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/8t7hpp95vhejj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1038/s41598-026-73340-x",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73340-x"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Integrated classical/quantum–classical neural networks, DFT, and experiments for predicting bandgap and color of CrxSbxTi1−2xO2 yellow-orange pigments",
  "title_text": "Integrated classical/quantum–classical neural networks, DFT, and experiments for predicting bandgap and color of CrxSbxTi1−2xO2 yellow-orange pigments",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/8t7hpp95vhejj",
  "venue": "Scientific Reports",
  "versions": []
}