{
  "abstract": "A molecular crystal structure is jointly described by its space group symmetry, unit cell, and the molecular alignment within the asymmetric unit. Concurrently predicting all three variables is a daunting task, as it mixes discrete symmetry choices with a high-dimensional search in the continuous space. To address this challenge, we decouple these variables using a three-step generation process. Specifically, we train a flow model to learn the conditional distribution of invariant lattice descriptors (e.g. direct- and reciprocal-lattice successive minima and Selling scalars) from a molecular graph, a Hall setting, and the number of molecules in the asymmetric unit ($Z'$). Using a two sequential quasi-random sampling processes, we first reconstruct the cell parameters that match the predicted lattice invariants and density requirements, and then conduct a molecular packing search within the give symmetry and unit cell constraint. On 84 single-component systems with $Z' \\le 1$, our approach reproduces experimental matches for 83 systems; the remaining failure stems from force-field limitations in preserving the experimental structure. These results demonstrate that learned cell proposals can effectively support crystal structure prediction (CSP) for a given Hall setting and $Z'$, which may be extended to fully blind prediction with variable symmetry and $Z'$ settings in the future.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.04193",
  "author_total": 2,
  "authors": [
    {
      "name": "Qiang Zhu"
    },
    {
      "name": "Yihan Weng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ege65mrnxww9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.04193",
  "fulltext_url": null,
  "id": "4ege65mrnxww9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.04193",
  "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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220655348",
  "pdf_url": "https://arxiv.org/pdf/2610.04193",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4ege65mrnxww9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.04193",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.04193"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Molecular Crystal Structure Prediction from Conditional Flow on the Unit Cells",
  "title_text": "Molecular Crystal Structure Prediction from Conditional Flow on the Unit Cells",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/4ege65mrnxww9",
  "venue": "arXiv",
  "versions": []
}