Multi-objective Bayesian optimization for experimental design in copolymerization and revealing chemical mechanism of Pareto fronts

The chemical properties of copolymers are strongly influenced by a number of intrinsic characteristics such as their molecular weight and their monomer composition ratio. Identifying the optimal conditions for the copolymerization process that results in a synthesized copolymer with the desired characteristics is a major challenge. Optimization of the copolymerization process has traditionally been based on trial-and-error approaches by humans relying on em-pirical rules. Thus, the design space that can be explored experimentally is severely limited under time and economic constraints. In addition, solving problems such as nonuniformity of both temperature and the concentration of chemi-cals in the reaction field is also challenging. In this study, we established multi-objective Bayesian optimization and flow copolymerization systems to explore optimal copolymerization conditions for synthesizing copolymers that sim-ultaneously exhibit multiple target characteristics. Finally, we visualized Pareto fronts representing trade-offs be-tween polymer characteristics and employed quantum chemical calculations to reveal chemical origins of Pareto fronts.