{
  "abstract": "Accurate and interpretable property prediction is essential for exploring hybrid organic-inorganic perovskites. Here, we combine machine-learning screening with SHAP analysis and SISSO descriptor discovery. Using 4,456 compounds, optimized XGBoost models achieved test-set $R^2$ values of 0.99 for formation energy and 0.93 for band gap and were applied to over 180,000 candidates. Comparison across approximately 17,000 common compounds revealed broadly overlapping distributions from XGBoost and CGCNN but substantially lower predictions from UPET, suggesting potential limitations in its transferability to hybrid organic-inorganic perovskites. SHAP analysis and correlation-based pruning retained eight primary features for each property, from which SISSO derived expressions comprising four nonlinear descriptors. These expressions couple molecular and inorganic properties for formation energy and emphasize relative atomic sizes and B-site characteristics for band gap. This framework supports efficient candidate prioritization and interpretable analysis of learned structures-property relationships in hybrid perovskites.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.09944",
  "author_total": 3,
  "authors": [
    {
      "name": "Christopher Xia"
    },
    {
      "name": "Hao Zhang"
    },
    {
      "name": "Qingyuan Jin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8f6wfjmmycagf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.09944",
  "fulltext_url": null,
  "id": "8f6wfjmmycagf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.09944",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.09944",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8f6wfjmmycagf.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:23Z",
      "native_id": "2611.09944",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.09944"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Interpretable machine learning of formation energies and band gaps in hybrid perovskites through SHAP analysis and SISSO modelling",
  "title_text": "Interpretable machine learning of formation energies and band gaps in hybrid perovskites through SHAP analysis and SISSO modelling",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:23Z",
  "url": "https://research.draconitic.com/paper/8f6wfjmmycagf",
  "venue": "arXiv",
  "versions": []
}