Open AccessSummit: Benchmarking Machine Learning Methods for Reaction Optimisation
Author(s) -
Felton Kobi C.,
Rittig Jan G.,
Lapkin Alexei A.
Publication year - 2021
Publication title -
chemistry ‐ methods
Language(s) - English
Resource type - Journals
ISSN - 2628-9725
DOI - 10.1002/cmtd.202000051
Subject(s) - benchmarking , computer science , intuition , summit , machine learning , bayesian optimization , artificial intelligence , bayesian probability , philosophy , epistemology , marketing , physical geography , business , geography
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.