Organic Chemistry

Multi-task Bayesian Optimization of Chemical Reactions

Authors

Abstract

Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous or similar chemical optimization studies. Therefore, BO can still require more iterations than many experimental budgets provide. Here, we overcome this challenge using multi-task BO. Through in silico benchmarking studies, we show how past experimental data can be leveraged to improve the quality and speed of reaction optimization.

Version notes

Error in the MTBO code was corrected ( see https://github.com/sustainable-processes/summit/pull/116). Figures updated, but no changes to conclusions.

Content

Thumbnail image of multitask_neurips.pdf

Supplementary weblinks