Combining automated microfluidic experimentation with machine learning for efficient polymerization design

Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and in situ infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste was decreased by two orders of magnitude and catalytic discovery was reduced from weeks to hours. Bayesian regularization backpropagation was used in conjunction with kinetic modeling to understand reaction space and resultant techno-economic topology. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction.