A multimodal robotic platform for multi-element electrocatalyst discovery.
basic_science · Level V
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- Record sourced from PubMed, PMID 40987343.
- Also identified by DOI 10.1038/s41586-025-09640-5.
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Abstract
One of the goals of 'AI for Science' is to discover customized materials through real-world experiments. Pioneering advances have been made in computational predictions and the automation of materials synthesis<sup>1-7</sup>. Yet most materials experimentation remains constrained to using unimodal active learning approaches, relying on a single data stream. The potential of artificial intelligence to interpret experimental complexity remains largely untapped<sup>8,9</sup>. Here we present Copilot for Real-world Experimental Scientists (CRESt), a platform that integrates large multimodal models (incorporating chemical compositions, text embeddings and microstructural images) with knowledge-assisted Bayesian optimization and robotic automation. CRESt uses 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 enables monitoring with cameras and the generation of vision-language-model-driven hypotheses to diagnose and correct experimental anomalies. Applied to electrochemical formate oxidation, CRESt explored more than 900 catalyst chemistries and 3,500 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) that exhibits a 9.3-fold improvement in cost-specific performance.