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. Here we present SynA...
Izumi Takahara, K. Nishio, Akira Aiba et al.
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