TFMat用文本控制晶体生成，在MP-20基准达到92.04%匹配率，比CrystalFlow更高效。
研究人员推出TFMat框架，使用结构化材料语言作为语义先验。在Perov-5、Carbon-24和MP-20基准测试中，TFMat将单候选匹配率提升至92.04%。该框架在从头生成中改善了元素计数和密度分布对齐，同时保持组成选择输出的粗略属性一致性。
Text-guided flow matching enables sample-efficient crystal structure generation
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.