A multimodal robotic platform for multi-element electrocatalyst discovery.

One of the goals of 'AI for Science' is to discover customized materials through real-world experiments. Pioneering advances have been achieved in computational predictions and the automation of materials synthesis1-7. Yet, most materials experimentation remains constrained to using unimodal active learning (AL) approaches, relying on a single data stream. The potential of AI to interpret experimental complexity remains largely untapped8,9. Here we present Copilot for Real-world Experimental Scientists (CRESt), a platform that integrates large multimodal models (LMMs, incorporating chemical compositions, text embeddings, and microstructural images) with Knowledge-Assisted Bayesian Optimization (KABO) and robotic automation. CRESt employs knowledge-embedding-based search space reduction and adaptive exploration-exploitation strategy to accelerate materials design, high-throughput synthesis and characterization, and electrochemical performance optimization. CRESt allows monitoring with cameras and vision-language-model-driven hypothesis generation to diagnose and correct experimental anomalies. Applied to electrochemical formate oxidation, CRESt explored over 900 catalyst chemistries and 3500 electrochemical tests within 3 months, identifying a state-of-the-art catalyst in the octonary chemical space (Pd-Pt-Cu-Au-Ir-Ce-Nb-Cr) which exhibits a 9.3-fold improvement in cost-specific performance.