用约束生成框架精准设计晶体材料，提高稳定性和生产可行性。
A Generation Framework with Strict Constraints for Crystal Materials Design
- 基于大模型生成对称性与组分比等中间约束，控制生成过程。
- 生成结构满足目标性质的概率超现有方法两倍以上。
- 几乎100%符合预设化学组成，降低生产供应链风险。
晶体材料设计在新能源、生物医学和半导体等领域至关重要。近年来数据驱动方法实现了多样化晶体结构的生成，但多数方法仍依赖随机采样，需多次后处理才能筛选出具有特定物理化学性质的稳定候选物。本文提出一种新的约束生成框架，将多种约束作为输入，实现针对特定化学与性能的晶体结构生成。该框架通过大语言模型（LLMs）生成中间约束，如对称性信息和组分比，以匹配目标性质；随后由晶体结构生成器利用这些约束，确保生成过程受控。实验表明，生成结构满足目标性质的概率超过现有方法两倍；同时近100%生成晶体严格遵循预设化学组成，显著降低生产环节中的供应链风险。
原文摘要 · Abstract (English)
The design of crystal materials plays a critical role in areas such as new energy development, biomedical engineering, and semiconductors. Recent advances in data-driven methods have enabled the generation of diverse crystal structures. However, most existing approaches still rely on random sampling without strict constraints, requiring multiple post-processing steps to identify stable candidates with the desired physical and chemical properties. In this work, we present a new constrained generation framework that takes multiple constraints as input and enables the generation of crystal structures with specific chemical and properties. In this framework, intermediate constraints, such as symmetry information and composition ratio, are generated by a constraint generator based on large language models (LLMs), which considers the target properties. These constraints are then used by a subsequent crystal structure generator to ensure that the structure generation process is under control. Our method generates crystal structures with a probability of meeting the target properties that is more than twice that of existing approaches. Furthermore, nearly 100% of the generated crystals strictly adhere to predefined chemical composition, eliminating the risks of supply chain during production.
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