The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery.

翻译：新催化剂的发现对于设计更高效的新型化学工艺、实现可持续未来至关重要。我们提出了一种融合语言推理与基于量子化学的三维原子表征反馈的AI引导计算筛选框架。该方法将催化剂发现视为不确定环境下的搜索任务：智能体通过迭代结合大语言模型（LLM）生成的假设与基于原子图神经网络（GNN）的反馈，主动寻找高效催化剂。中间搜索步骤中识别的催化剂需基于空间取向、反应路径和稳定性进行结构评估。基于吸附能与能垒的评分函数可引导LLM知识空间中的探索向能量有利、高效率催化剂方向倾斜。我们提出的规划方法无需人工干预即可自动引导探索，在性能上可与专家枚举的化学描述符方法相媲美。通过融合语言引导推理与计算化学反馈，本工作开创了AI加速、可信赖的催化剂发现新范式。