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Summit: Benchmarking Machine Learning Methods for Reaction Optimisation

preprint
submitted on 10.09.2020 and posted on 11.09.2020 by Kobi Felton, Jan Rittig, Alexei Lapkin

In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail

to find optimal solutions.

History

Email Address of Submitting Author

aal35@cam.ac.uk

Institution

University of Cambridge

Country

United Kingdom

ORCID For Submitting Author

0000-0001-7621-0889

Declaration of Conflict of Interest

Authors declare no conflicts of interest

Version Notes

Version being submitted for peer review

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