{
  "abstract": "Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, its performance remains limited by the complex crystal structures and diverse chemical compositions. In this work, we develop UFO-MGen, a universal flow-based generative model that learns topological features of Wyckoff representations and leverages this information to accurately generate crystals across vast structural and chemical spaces. Compared with state-of-the-art generative models, UFO-MGen achieves the highest crystal generation success rate under a rigorous multi-stability evaluation framework, the highest SUN (stable, unique, novel) rate, and a remarkable extrapolation capability that has not been reported by previous models. Furthermore, a fine-tuning module is implemented to UFO-MGen for property-constrained crystal generation, enabling the inverse materials design toward target properties. The UFO-MGen opens a new avenue for accelerated materials discovery and providing a foundation for universal materials intelligence.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.26547",
  "author_total": 4,
  "authors": [
    {
      "name": "Jingyi Zhou"
    },
    {
      "name": "Oyshee Chowdhury"
    },
    {
      "name": "Noah Oyeniran"
    },
    {
      "name": "Chongze Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8bzrjyyp9afr1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.26547",
  "fulltext_url": null,
  "id": "8bzrjyyp9afr1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.26547",
  "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"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214066817",
  "pdf_url": "https://arxiv.org/pdf/2609.26547",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8bzrjyyp9afr1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.26547",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.26547"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Topology-Stratified Materials Discovery with A Flow-Based Generative Model",
  "title_text": "Topology-Stratified Materials Discovery with A Flow-Based Generative Model",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/8bzrjyyp9afr1",
  "venue": "arXiv",
  "versions": []
}