Summit: Benchmarking Machine Learning Methods for Reaction Optimisation

ChemRxiv

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

2. Jan G. Rittig Aachen

   RWTH Aachen University

   Affiliation as printed

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

3. Alexei A. Lapkin

   University of Cambridge

   Affiliation as printed

   University of Cambridge