{
  "abstract": "Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials design. Text provides a compact way to combine composition, symmetry, prototype and property cues, yet it has not been clear whether such information can steer flow-based crystal generation. Here we introduce TFMat, a text-conditioned flow-matching framework that uses structured materials language as a semantic prior for a CrystalFlow generator. Across Perov-5, Carbon-24 and MP-20 crystal structure prediction benchmarks, TFMat improves one-candidate match rates over CrystalFlow and reaches a 92.04% MP-20 match rate with 20 candidates; in de novo generation, it improves element-count and density distribution alignment while retaining coarse property consistency in composition-selected outputs. These results position structured text as an inspectable control layer for translating human-readable materials intent into candidate crystals for downstream simulation and validation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01076",
  "author_total": 1,
  "authors": [
    {
      "name": "Wentao Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpkzwy4nwhsxp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01076",
  "fulltext_url": null,
  "id": "cpkzwy4nwhsxp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01076",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/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": "W7206162495",
  "pdf_url": "https://arxiv.org/pdf/2609.01076",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cpkzwy4nwhsxp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.01076",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01076"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Text-guided flow matching enables sample-efficient crystal structure generation",
  "title_text": "Text-guided flow matching enables sample-efficient crystal structure generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/cpkzwy4nwhsxp",
  "venue": "arXiv",
  "versions": []
}