Summit: Benchmarking Machine Learning Methods for Reaction Optimisation

Chemistry - Methods, vol. 1, pp. 116–122

Abstract

Abstract 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.

Authors 3

1. Kobi Felton

   University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering University of Cambridge Cambridge UK

   Department of Chemical Engineering, University of Cambridge, Cambridge, UK

   These authors contributed equally to this work

2. Jan G. Rittig Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Process Systems Engineering (AVT.SVT) Aachen 52074 Germany

   RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen, 52074 Germany

   These authors contributed equally to this work

3. Alexei A. Lapkin corresponding

   University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering University of Cambridge Cambridge UK

   Department of Chemical Engineering, University of Cambridge, Cambridge, UK