AI/ML Strategies in polymer materials: Continuous flow polymer synthesis and reactions

The use of artificial intelligence and machine learning (AI/ML) in polymer materials have appended the ability to rapidly optimize synthetic routes and manufacturing. The use of Bayesian and statistical methods enable 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 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 development of a feed-back loop learning for possible deep learning (DL) development.