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Multi-task Bayesian Optimization of Chemical Reactions

preprint
revised on 19.04.2021, 14:27 and posted on 20.04.2021, 05:39 by Kobi Felton, Daniel Wigh, Alexei Lapkin
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.

History

Email Address of Submitting Author

kcmf2@cam.ac.uk

Institution

University of Cambridge

Country

United Kingdom

ORCID For Submitting Author

0000-0002-3616-4766

Declaration of Conflict of Interest

Authors declare no conflicts of interest.

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.

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