Towards AI-Controlled Continuous Flow Chemistry and Polymerizations

The study of structure-composition-property (SCP) relationships in soft matter is of high interest since advances in simulation and the use of statistical optimization methods have been widely used. The macroscopic properties can also be defined by the processing methods and therefore more finite element analysis (FEA) methods have found wide utilization. The use of artificial intelligence and machine learning (AI/ML) in polymer materials has appended the ability to rapidly optimize synthetic routes and manufacturing. The use of Bayesian and statistical methods enables the application of logic-derived design and regression analysis into an otherwise trial-and-error approach in polymer synthesis, fabrication, and characterization. We demonstrate in this talk the use of continuous flow reaction chemistry and polymerizations to enable unit operation optimization and the possibility of autonomous design and synthesis with a hierarchical approach and learning. There is a high possibility that a combination of P, V, T, and flow rate control enables new methods of copolymerization and the ability to use kinetics as a handle for optimized macromolecular properties and design for controlled yield. The automation for online monitoring is a possibility with improved instrumentation and the development of a feedback loop learning for possible deep learning (DL) development.