From Chatbots to Chemists: Autonomous Agents for Heterogeneous Catalysis

Abstract Large language models (LLMs) face inherent limitations in complex scientific workflows, particularly in heterogeneous catalysis, due to a lack of tool integration, autonomous planning, and closed-loop interaction with robotic platforms. While autonomous agent systems have emerged to bridge these gaps, the rapid proliferation of diverse architectural paradigms has fragmented the literature. This review provides a systematic integration of model capabilities, system design, and catalytic applications. We analyze the evolutionary roadmap across three phases: foundational LLMs (Phase 1) for passive knowledge processing, tool-equipped single-agent systems (SAS) for autonomous execution (Phase 2), and collaborative multi-agent systems (MAS) for distributed catalyst discovery (Phase 3). We critically evaluate LLM capabilities in chemical reasoning, the expansion of functionality through tool use in SAS, and the realization of closed-loop discovery via MAS. Furthermore, we assess existing benchmarking frameworks and propose standardized metrics for evaluating AI agents in catalysis. By establishing this evolutionary roadmap, this review highlights the transition of AI from conversational chatbots to specialized digital chemists, bridging the gap between passive information processing and autonomous discovery in heterogeneous catalysis.