A method based on continuous Bayesian optimization of monomer feed into a semi-batch copolymerization is demonstrated that allows to counter the composition drift in copolymerizations stemming from disparate reactivity ratios. The method requires online monitoring of the reaction, but requires no prior kinetic knowledge on the copolymerization or any modelling of the polymerizations, making this the first method is generally applicable to any copolymerization system to achieve this aim. Copolymerizations between acrylates and methacrylates and styrene are demonstrated to achieve perfectly statistical and homogenous distributions, and the radical ring opening copolymerisation between a cyclic ketene acetal and methyl methacrylate is showcased as an example of a challenging copolymerization where countering the composition drift results in a completely degradable material, paving a pathway to new sustainable polymers in the future. Next to perfect regulation of the sequence distribution in these copolymers, we also demonstrate how the method can be applied to create non-natural composition drifts in polymers at will.
