Autonomous Chemistry and Materials Innovation Driven by Scientific Agents

Large language model (LLM)-based agents are reshaping how self-driving laboratories (SDLs) may support autonomous chemical and materials research. Although SDLs have enabled major advances in mechanized experimentation and closed-loop optimization, their scientific utility remains limited when tasks require literature-grounded reasoning, adaptive coordination, and interpretation beyond predefined search spaces. In this perspective, we examine how LLM-based agents may help bridge this gap by translating scientific intent into machine-executable workflows. We propose a five-module framework─Comprehension, Design, Execution, Analysis, and Optimization─to organize the capabilities required for agent-enabled SDLs, and we discuss representative systems, including Coscientist, ChemCrow, LLM-RDF, and AI-Chemist, as milestones in this transition. We also emphasize that agent-enabled SDLs should not be conflated with autonomous scientific discovery. Safety in physical execution, hardware interoperability, reproducibility, and auditability remain central challenges. To support a more critical assessment, we introduce the HYDRA framework for benchmarking trustworthy agent-enabled workflows. Finally, we outline a human–AI–SDL collaborative model in which scientists remain responsible for scientific framing, interpretation, and oversight.