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

2026-09-01

AI Summary

通过引入TFMat，一种文本引导的流匹配框架，改善了晶体结构生成的样本效率和控制接口问题，提高了晶体预测准确率。

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.

Problem

• text-guided
• crystal generation
• control interface
• materials design
• flow-based

Innovation

• text-guided flow matching
• crystal structure generation
• structured materials language
• sample efficiency
• semantic prior