{
  "abstract": "Large-scale crystal databases have transformed materials discovery by making millions of structures and thermodynamic properties accessible to materials informatics. Yet dynamical stability remains sparsely characterized at a comparable scale because its evaluation is substantially more computationally demanding than conventional structure and energy calculations. This mismatch creates a critical gap between the rapidly expanding crystal-structure space and the physical information available for materials research and machine learning. Here we introduce Crystoria, a large-scale crystal dataset designed to bring dynamical stability to the scale of modern materials data. We construct Crystoria by systematically curating low-energy and metastable inorganic crystals, standardizing their structural identities, and combining machine-learning-accelerated structural relaxation with phonon-based dynamical screening. The resulting dataset contains 2,649,640 crystal structures, spanning 89 elements, 219 of the 230 space groups, and primitive cells containing 1-48 atoms. Among them, 1,793,353 structures (67.68%) are classified as dynamically stable, revealing substantial variation in dynamical stability across chemical and crystallographic space. By bringing dynamical stability to the scale of millions of crystal structures, Crystoria provides a physically grounded foundation for materials artificial intelligence (AI) and enables systematic exploration of the dynamical crystal landscape.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.38195",
  "author_total": 4,
  "authors": [
    {
      "name": "Ke-Xin Pang"
    },
    {
      "name": "Ze-Feng Gao"
    },
    {
      "name": "Yan Gao"
    },
    {
      "name": "Zhong-Yi Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ewp0gqrt2ktwx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38195",
  "fulltext_url": null,
  "id": "ewp0gqrt2ktwx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.38195",
  "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/2610.38195",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ewp0gqrt2ktwx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.38195",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38195"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Crystoria: A Large-Scale Crystal Dataset Linking Thermodynamic and Dynamical Stability for Materials AI",
  "title_text": "Crystoria: A Large-Scale Crystal Dataset Linking Thermodynamic and Dynamical Stability for Materials AI",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:44Z",
  "url": "https://research.draconitic.com/paper/ewp0gqrt2ktwx",
  "venue": "arXiv",
  "versions": []
}