How Researchers’ Words Become Experimental Plans (IMAGE)
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When researchers share their experience in everyday language, such as “Increasing the temperature improves yield,” a large language model (LLM) translates their input into structured prior information that AI can use. The Bayesian optimization AI then uses this knowledge to select the next experiment. Experimental results are fed back to the AI for further learning, while researchers receive updates and can revise their guidance in natural language at any time. An adaptive weight credibility detection (AWCD) mechanism also monitors whether the guidance aligns with experimental data and automatically disables misleading prior information when sufficient contradictory evidence emerges.
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Korea Research Institute of Chemical Technology(KRICT)
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