ChemAI: Empowering Robots to Automate Chemical Experiments with Large Language Models

Chemical experimentation remains fundamental to material discovery and compound synthesis, yet conventional methods face critical limitations. Manual laboratory workflows require repetitive operations that prolong research timelines, while robotic automation platforms inadvertently shift the burden to chemists through complex programming requirements. Existing systems also struggle to adapt to novel experimental designs, constraining scientific innovation. To bridge these gaps, we present a large language model (LLM)-driven robotic framework that redefines automated experimentation. Our system interprets natural language instructions through advanced LLM processing, dynamically integrates real-time environmental data, and autonomously generates executable code utilizing a modular action library. A self-correcting validation mechanism ensures operational precision, systematically resolving execution errors through iterative feedback. Empirical validation across two benchmark experiments including the iodine clock reaction kinetics and the salt purification demonstrates transformative advantages: 1) intuitive interaction; 2) extended workflow handling efficiency gain in multi-step protocols; 3) adaptive code generation. This paradigm shift may enable chemists to focus on hypothesis-driven research rather than procedural implementation, establishing a scalable infrastructure for next-generation autonomous laboratories.