{
  "abstract": "Crystal-generation models can explore compositions and atomic arrangements beyond a fixed set of crystal prototypes, but their usefulness for a specific materials family depends on whether the generated structures retain the defining topology and whether their predicted properties remain reliable across the sampled chemistry. Here, a pretrained diffusion model is used to generate candidate ABX$_3$ perovskites and to examine their structural, chemical and electronic-property distributions. A connectivity-based filter distinguishes three-dimensional corner-sharing perovskites from structures that share the same nominal ABX$_3$ stoichiometry. The accepted set contains substantial B-site multiplicity, including ordered double-perovskite configurations that are inaccessible to the two-prototype substitution reference used here. The generated set is enriched in chloride-, bromide- and iodide-containing compositions relative to the Materials Project, while all generated B-site elements are already represented in that database. On the downstream MP-trained ALIGNN scale, the predicted bandgap distribution is shifted towards 1.4 eV, but the target-window fraction (8.2\\%) is close to the MP reference fraction (7.3\\%); the separate MatterGen and ALIGNN label sources prevent this comparison from establishing target-condition enrichment. An ALIGNN model performs well on a formula-grouped random test split but does not outperform a constant baseline on a deliberately severe B-site-disjoint test. First-principles calculations on six generated structures show that relaxation can alter structural classification and that predicted and calculated gaps can differ appreciably. These results identify both the chemical-space coverage offered by crystal diffusion and the structural and predictive checks needed before its outputs can be treated as materials candidates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00766",
  "author_total": 2,
  "authors": [
    {
      "name": "Sunraj Patel"
    },
    {
      "name": "Krishanu Biswas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dtw16b6jdm867.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00766",
  "fulltext_url": null,
  "id": "dtw16b6jdm867",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00766",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00766",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dtw16b6jdm867.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.00766",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00766"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Diffusion-Based Generative Screening of ABX$_3$ Perovskite Compositions for Bandgap-Targeted Materials Discovery",
  "title_text": "Diffusion-Based Generative Screening of ABX_3 Perovskite Compositions for Bandgap-Targeted Materials Discovery",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/dtw16b6jdm867",
  "venue": "arXiv",
  "versions": []
}