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. …
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