AI 中文总结
LATHE通过将七类晶体学性质表达为可微目标实现晶体结构直接编辑，并集成于多智能体系统，实现自然语言驱动的闭环材料设计，在基准和带隙逆向设计中表现优异。
AI 中文摘要
操纵晶体结构以使其具有目标几何性质或对称性约束是一个长期存在的挑战。现有的计算框架存在不足：大语言模型（LLM）空间感知能力差，且在标记空间中操作粒度较粗；而基于扩散和梯度的结构生成方法通常从头生成完整结构，而非允许在任意约束下对给定输入进行细粒度编辑。我们提出了LATHE，一个几何工具包，通过将七类晶体学性质——键长、键角、二面角、配位环境、晶格参数、晶胞体积和空间群——表达为可微目标函数，从而实现对晶体结构的直接几何编辑，弥补了这一差距。为了展示其在材料设计中的应用，我们进一步通过模型上下文协议（Model Context Protocol）服务器公开LATHE，并将其嵌入一个闭环多智能体系统中，该系统将假设转化为自然语言中的几何修改，以朝向给定的设计目标。在单性质基准测试中，LATHE在所有七种性质类型上实现了近乎完美的约束满足，同时使优化后的结构接近局部能量最小值。配备LATHE的智能体将超过87.5%的自然语言提示转化为有效的可执行配置，并忠实地完成它们。在带隙逆向设计案例研究中，多智能体循环在十次独立运行中的九次达到了目标容差窗口，典型成本为十三个假设评估周期。通过连接自然语言假设生成和基于物理的梯度结构编辑，这项工作为可解释的闭环计算材料发现建立了一种范式，可立即应用于广泛的功能材料设计任务。
英文摘要
Manipulating crystal structures towards targeted geometric properties or symmetry constraints is a longstanding challenge. Existing computational frameworks fall short: LLMs have poor spatial awareness and operate at coarse granularity in the token space, while diffusion- and gradient-based structure generation approaches generally produce complete structures \textit{de novo} rather allowing for fine-grained editing of a given input under arbitrary constraints. We present LATHE, a geometric toolkit that closes this gap by expressing seven classes of crystallographic properties --- bond length, bond angle, dihedral angle, coordination environment, lattice parameters, cell volume, and space group, as differentiable objectives to enable direct geometric editing of crystal structures. To demonstrate its usage in materials design, we further expose LATHE through a Model Context Protocol server and embed it in a closed-loop multi-agent system which translates hypotheses intogeometric modifications in natural language towards a given design objective. Across single-property benchmarks, LATHE attains near-perfect constraint satisfaction on all seven property types while keeping optimized structures close to local energy minima. The LATHE-equipped agent translates over 87.5\% of natural-language prompts into valid executable configurations and faithfully completes them. In a band-gap inverse-design case study, the multi-agent loop reaches the target tolerance window in nine of ten independent runs at a typical cost of thirteen hypothesis-evaluation cycles. By bridging natural-language hypothesis generation and physically grounded gradient-based structural editing, this work establishes a paradigm for interpretable, closed-loop computational materials discovery that is immediately applicable to a broad class of functional material design tasks.
