Autocata：基于能力中心型人工智能的反应网络引导的自主催化剂筛选
Autocata: Reaction-network-guided autonomous catalyst screening via capability-centric AI
- Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所界面物理与技术重点实验室）
- Photon Science Research Center for Carbon Dioxide, Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高级研究院二氧化碳光子科学研究中心）
- State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高级研究院低碳催化与二氧化碳利用国家重点实验室）
- Titan Holdings（泰坦控股）
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Autocata是一种知识嵌入的自主智能体，通过将反应路径编码为可调用的能力，利用预训练Transformer和79个生成模型，实现从自然语言提示到催化剂筛选的全自动流程，降低专家级发现门槛。
AI中文摘要：
人工智能（AI）扩展了催化剂发现的可及空间，但执行复杂的筛选活动仍需要持续的人工干预，以将高层次的反应目标与底层的计算流程衔接起来。在此，我们提出了Autocata，一种知识嵌入的自主智能体，通过将多步反应路径编码为可重用、可语言调用的能力，来扩展催化专业知识。该系统由一个在二百万个催化剂-吸附质结构上预训练的Transformer驱动，并包含一个能力注册表，其中包含79个吸附质专用的生成模型，涵盖碳、氮、氧和氢中间体的复杂反应网络。给定一个高层的自然语言提示，Autocata会解构反应网络，查询并配置匹配的生成模型，生成候选表面构型，强制执行物理和几何有效性过滤，并协调机器学习势评估以对有前景的材料进行排序。关键的是，该智能体在遇到能力边界或任务瓶颈时会自适应地重新配置工作流程。我们在CH4活化、氮还原反应（N2RR）和CO2转化为甲醇的路径中展示了这些能力。通过从静态工具包转向自适应的、语言驱动的计算基础设施，Autocata降低了专家级催化剂发现的门槛，并为自驱动催化研究提供了一种可扩展的范式。
英文摘要：
Artificial intelligence (AI) has expanded the accessible space for catalyst discovery, yet executing complex screening campaigns still requires constant human intervention to bridge high-level reaction objectives with underlying computational pipelines. Here, we present Autocata, a knowledge-embedded autonomous agent that scales catalytic expertise by encoding multistep reaction pathways into reusable, language-invocable capabilities. Powered by a Transformer pretrained on two million catalyst-adsorbate structures, the system incorporates a capability registry of 79 adsorbate-specialized generative models spanning complex reaction networks across carbon, nitrogen, oxygen, and hydrogen intermediates. Given a high-level natural-language prompt, Autocata deconstructs reaction networks, queries and configures matching generative models, generates candidate surface configurations, enforces physical and geometric validity filtering, and coordinates machine-learning potentials evaluations to rank promising materials. Crucially, the agent adaptively reconfigures workflows when encountering capability boundaries or task bottlenecks. We demonstrate these capabilities across CH4 activation, nitrogen reduction reaction (N2RR), and CO2-to-methanol pathways. By shifting from static toolkits to an adaptive, language-driven computational infrastructure, Autocata lowers the barrier for expert-level catalyst discovery and offers a scalable paradigm for self-driving catalytic research.
