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 multiobjective 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 semibatch emulsion copolymerization, targeting a specific particle
size and full conversion.