Dynamic flow experiments for Bayesian optimization of a single process objective

Abstract

A new method, named dynamic experiment optimization (DynO), is developed for the current needs of chemical reaction optimization by leveraging for the first time both Bayesian optimization and data-rich dynamic experimentation in flow chemistry. DynO is readily implementable in automated systems and it is augmented with simple stopping criteria to guide non-expert users in fast and reagent-efficient optimization campaigns. The developed algorithms is compared in silico with the algorithm Dragonfly and an optimizer based on random selection, showing remarkable results in Euclidean design spaces superior to Dragonfly. Finally, DynO is validated with an ester hydrolysis reaction on an automated platform showcasing the simplicity of the method.