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Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents
Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the measurements reduced to predefined scalar objectives and the reasons behind success left unarticulated.
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