{
  "abstract": "Double-perovskite oxides (A2BB′O6) are attractive for photovoltaic and optoelectronic applications, but their bandgap depends on a compositional space too large for routine DFT screening. In this study, we build composition-only machine-learning models on 1,306 DFTcomputed (GLLB-SC) double perovskites, using 72 descriptors derived from elemental electronegativity, ionic/atomic radius, group and period position, Mendeleev number, and first ionization energy. Six regression algorithms were compared under 10-fold cross-validation where a tuned XGBoost model gave the best results (test R2 = 0.975, MAE = 0.188 eV, RMSE = 0.245 eV), outperforming all others with statistical confidence (Friedman χ2 = 45.14, p =1.4×10-7; Wilcoxon p < 0.005 throughout). SHAP analysis pointed to A-site group number and B-site electronegativity and radius as the main drivers of bandgap, matching the mechanisms reported in the original DFT study. These results demonstrate that interpretable, composition-only machinelearning models can reproduce DFT-level bandgap trends at negligible computational cost, providing a practical high-throughput screening tool for lead-free double-perovskite discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Industrial and Production Engineering (IPE",
    "Bangladesh Army University of Science and Technology",
    "Department of Materials and Metallurgical Engineering",
    "Bangladesh University of Engineering and Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Md Ragib Abid",
      "orcid": "0009-0003-9137-4330"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Md Shafayatul Islam"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ytshyybmq03g.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010932/v1",
  "fulltext_url": null,
  "id": "4ytshyybmq03g",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010932/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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223402345",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010932/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4ytshyybmq03g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15010932",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010932/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Machine-Learning-Guided Prediction and Statistical Analysis of Electronic Bandgaps in Double-Perovskite Oxides for Photovoltaic and Optoelectronic Design",
  "title_text": "Machine-Learning-Guided Prediction and Statistical Analysis of Electronic Bandgaps in Double-Perovskite Oxides for Photovoltaic and Optoelectronic Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/4ytshyybmq03g",
  "venue": "ChemRxiv",
  "versions": []
}