Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

Abstract

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multi-objective target optimization with an explicit aim to minimize the number of ‘expensive’ experiments, guides the discovery process. This ‘black-box’ approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semi-batch emulsion copolymerization, targeting a specific particle size and full conversion.