用大模型自动完成金属有机框架材料从文献到筛选的全流程发现。
An LLM agent for end-to-end computational materials discovery
- 基于大模型构建全流程自动化代理，整合文献、结构与计算任务。
- 在湿烟气分离场景下发现候选材料，均来自互不相关的研究。
- 可跨领域连接分散的计算流程，发现传统方法忽略的高性能材料。
多尺度任务协同是计算材料发现的有效策略，但不同算法与工具的反复调用使其实施困难。我们提出MAESTRO，一个大型语言模型（LLM）代理系统，能够执行金属有机框架（MOFs）的完整筛选流程。该系统处理大量MOF文献，将相关论文与晶体结构关联，并将结果整理为可计算数据库，随后采用逐步提高计算成本的策略进行筛选。在湿烟气条件下筛选出的优质候选材料均源自无关研究。通过连接计算材料发现中异构的各阶段，基于大模型的代理系统可在多个应用领域间运作，发现传统筛选方法难以考虑的高性能材料。
原文摘要 · Abstract (English)
The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorithms and tools renders it challenging. We report MAESTRO, a large language model (LLM) agent system capable of executing the entire screening pipeline for metal-organic frameworks (MOFs). It processes a large body of MOF literature, links relevant publications to their crystal structures, and curates the results into a computation-ready database, which is then screened through a strategy of progressively increasing computational cost. The promising candidates identified for separation under wet flue gas conditions all originate from unrelated studies. By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.
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