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The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multi-agent system, ChemAgents, based on an on-board Llama-3-70B LLM, capable of executing complex, multi-step experiments with minimal human intervention. It operates through a Task Manager agent that interacts with human researchers and coordinates four role-specific agents—Literature Reader, Experiment Designer, Computation Performer, and Robot Operator—each leveraging one of four foundational resources: a comprehensive Literature Database, an extensive Protocol Library, a versatile Model Library, and a state-of-the-art Automated Lab. We demonstrate its versatility and efficacy through six experimental tasks of varying complexity, ranging from straightforward synthesis and characterization to more complex exploration and screening of experimental parameters, culminating in the discovery and optimization of functional materials. Our multi-agent-driven robotic AI chemist showcases the potential of on-demand autonomous chemical research to drive unprecedented efficiencies, accelerate discovery, and democratize access to advanced experimental capabilities across academic disciplines and industries.
Song et al. (2024) studied this question.