用AI代理自动筛选电催化材料，效率远超人工试错。
Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent
- 基于大模型的智能代理自主完成材料筛选全流程
- 在三种反应中平均1.4到3.4次尝试即找到有效催化剂
- 适合材料研发人员快速发现未报道的高潜力候选材料
电化学反应如氧还原（ORR）、氮还原（NRR）和二氧化碳还原（CO2RR）的催化剂发现仍是化学与材料科学的核心挑战。机器学习原子间势（MLIPs）和图神经网络模型可使单个吸附能计算速度提升数个数量级，但仍受制于人工决策：候选物选择、表面结构构建、吸附位点枚举、描述符失效分析及后续优化。本文提出Catalyst-Agent，一个基于模型上下文协议（MCP）服务器的大型语言模型驱动代理，实现闭环催化剂筛选自动化。该代理通过OPTIMADE搜索材料库，构建表面结构，利用Meta FAIRchem的UMA MLIP在AdsorbML中计算吸附能，评估反应特异性描述符，并对近似候选物进行结构优化。在ORR、NRR和CO2RR任务中，平均每成功一个材料需1.40–3.41次尝试。它识别出Sn3Sc、Sn3Y、Tl3La、Pb3Y和In3Y作为新的CO2RR候选材料，此前未见文献报道。对代表性NRR和CO2RR候选物的单点DFT验证确认了筛选结果。消融实验表明性能提升源于化学启发的候选选择与反馈驱动的优化，而非盲目遍历：完全随机筛选的成功率分别降至13.3%、16.7%和0%。结果表明，工具赋能的LLM代理可将催化剂筛选从人工试错转向更自主、可重复且自适应的工作流程。
原文摘要 · Abstract (English)
The discovery of catalysts for electrochemical applications such as the oxygen reduction reaction (ORR), nitrogen reduction reaction (NRR), and CO2 reduction reaction (CO2RR) remains a central challenge in chemistry and materials science. Machine-learning interatomic potentials (MLIPs) and graph neural network models now accelerate individual adsorption-energy calculations by orders of magnitude relative to density functional theory. However, true large-scale screening is still blocked by human decisions: selecting candidates, constructing slabs, enumerating adsorption sites, interpreting descriptor failures, and choosing follow-up modifications. Here, we introduce Catalyst-Agent, a Model Context Protocol (MCP) server-based, LLM-powered agent that autonomously coordinates closed-loop catalyst screening. Catalyst-Agent searches materials databases through OPTIMADE, constructs slabs, computes adsorption energies using Meta FAIRchem's UMA MLIP within AdsorbML, evaluates reaction-specific descriptors, and applies structural modifications to refine near-miss candidates. In ORR, NRR, and CO2RR campaigns, Catalyst-Agent demonstrates high performance and converges in 1.40-3.41 trials per successful material on average. It identified Sn3Sc, Sn3Y, Tl3La, Pb3Y and In3Y as CO2RR candidates for further validation that were not previously reported in the literature. DFT single-point checks confirmed screening outcomes for representative NRR and CO2RR candidates. Ablations show these gains arise from chemically informed candidate selection and feedback-directed modification rather than brute-force evaluation: fully randomized screening dropped to 13.3%, 16.7%, and 0% success for ORR, NRR, and CO2RR, respectively. These results show that tool-grounded LLM agents can shift catalyst screening from manual trial-and-error toward more autonomous, reproducible and adaptive workflows.
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