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Workshop: Adaptive Experimental Design and Active Learning in the Real World

BoChemian: Large Language Model Embeddings for Bayesian Optimization of Chemical Reactions

Bojana Rankovic · Philippe Schwaller


This paper explores the integration of Large Language Models (LLM) embeddings with Bayesian Op-timization (BO) in the domain of chemical reaction optimization with the showcase studyon Buchwald-Hartwig reactions. By leveraging llms, we can transform textual chemi-cal procedures into an informative feature space suitable for Bayesian optimization. Our findings show thateven out-of-the-box open-source LLMs can map chemical reactions for optimization tasks,highlighting their latent specialized knowledge. The results motivate the considerationof further model specialization through adaptive fine-tuning within the bo framework foron-the-fly optimization. This work serves as a foundational step toward a unified com-putational framework that synergizes textual chemical descriptions with machine-drivenoptimization, aiming for more efficient and accessible chemical research.The code is available at:

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