用多智能体大模型自主设计出突破传统规律的单原子催化剂。
Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
- 多个专用智能体协作，通过迭代推理与优化搜索催化剂。
- 发现打破反应中间体常规构效关系的高性能催化剂。
- 适合对催化设计与AI驱动材料发现感兴趣的科研人员。
大型语言模型（LLMs）正超越自然语言处理，在需人类专家经验的复杂科学任务中展现强大能力。在材料发现领域，LLMs通过推理和上下文学习能力，为传统机器学习方法所不具备的新范式。本文提出基于多智能体的电催化材料理性设计框架——MAESTRO，通过多个具有特定角色的LLM协同工作，自动搜索用于氧还原反应的高性能单原子催化剂。在自主设计循环中，各智能体不断推理、提出修改建议、反思结果并积累设计历史。借助这一迭代过程中的上下文学习，MAESTRO识别出未显式编码于背景知识中的设计原则，并成功发现突破反应中间体常规构效关系的催化剂。结果表明，多智能体LLM框架可有效生成化学洞见，推动有前景催化剂的发现。
原文摘要 · Abstract (English)
Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally require human expertise. This progress has extended into materials discovery, where LLMs introduce a new paradigm by leveraging reasoning and in-context learning, capabilities absent from conventional machine learning approaches. Here, we present a Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization (MAESTRO) framework in which multiple LLMs with specialized roles collaboratively discover high-performance single atom catalysts for the oxygen reduction reaction. Within an autonomous design loop, agents iteratively reason, propose modifications, reflect on results and accumulate design history. Through in-context learning enabled by this iterative process, MAESTRO identified design principles not explicitly encoded in the LLMs' background knowledge and successfully discovered catalysts that break conventional scaling relations between reaction intermediates. These results highlight the potential of multi-agent LLM frameworks as a powerful strategy to generate chemical insight and discover promising catalysts.
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