用可解释的结构单元语言，让AI自主设计新晶体结构。
Crystal-structure design by agentic AI in a language of motifs
- 用结构模式语言描述晶体，实现可解释的设计与修改
- 在同等验证预算下，发现新结构原型次数超生成模型3倍
- 适合材料设计、结构-性能关系研究者使用
数据驱动的材料发现具有较好的内插能力，但外推能力弱，极少能发现新结构类型。我们提出MatEvolve框架，通过代理式AI设计晶体，每个候选结构均附带明确推理理由，并进行验证。该代理以可解释的‘结构模式语言’进行思考，将每种晶体表示为‘模式谱’，描述其重复出现的几何特征——即‘模式’。模式谱不仅是描述工具，更是设计媒介：代理可编辑谱并重构晶体，最优候选由第一性原理计算验证。应用于稀土贫化永磁体设计时，仅基于未微调的Claude Fable 5语言模型，该方法在相同验证预算下发现新结构原型的次数超过生成模型三倍，且保有相当的磁性能达标率。此外，对所发现晶体的人类可读谱分析，揭示了结构-性能关联。
原文摘要 · Abstract (English)
Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types. We present MatEvolve, an agentic-AI framework designing crystals, proposing each candidate with a stated rationale and testing it. The agent reasons in an interpretable \emph{language of motifs}, writing each crystal as a \emph{motif profile} that describes the recurring geometric patterns---the \emph{motifs}---composing it. The motif profile serves not merely as a description of a material but as the medium for material design: the agent edits the profile and constructs a crystal from the modified one, and the most promising candidates are validated by first-principles calculation. Applied to the design of rare-earth-lean permanent magnets, MatEvolve---built on the state-of-the-art language model Claude Fable~5 without fine-tuning---reaches new structural prototypes more than three times as often as generative models under an equal validation budget, at a comparable on-target-magnet rate. Beyond design, analysing the discovered crystals' human-readable profiles reveals structure--property relationships.
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