@inproceedings{
rankovi{\'c}2023bochemian,
title={BoChemian: Large Language Model Embeddings for Bayesian Optimization of Chemical Reactions},
author={Bojana Rankovi{\'c} and Philippe Schwaller},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=A1RVn1m3J3}
}
This paper explores the integration of Large Language Models (LLM) embeddings with Bayesian Optimization (BO) in the domain of chemical reaction optimization with the showcase study on Buchwald-Hartwig reactions. By leveraging LLMs, we can transform textual chemical procedures into an informative feature space suitable for Bayesian optimization. Our findings show that even out-of-the-box open-source LLMs can map chemical reactions for optimization tasks, highlighting their latent specialized knowledge. The results motivate the consideration of further model specialization through adaptive fine-tuning within the bo framework for on-the-fly optimization. This work serves as a foundational step toward a unified computational framework that synergizes textual chemical descriptions with machine-driven optimization, aiming for more efficient and accessible chemical research.
The code is available at: https://github.com/schwallergroup/bochemian.
BoChemian: Large Language Model Embeddings for Bayesian Optimization of Chemical Reactions
Bojana Ranković Philippe Schwaller