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.

我们通过将计算机辅助筛选框架与语言推理和量子化学相结合，创造性地实现了人工智能加速的可靠催化剂发现。