Text-guided flow matching enables sample-efficient crystal structure generation

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.

Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)

arXiv:2609.01076 [cond-mat.mtrl-sci]

(or arXiv:2609.01076v1 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2609.01076 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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