Polymer Materials AI/ML Optimization—Continuous Flow Chemistry and Additive Manufacturing

Polymers are prevalent in materials and manufacturing in our society. Artificial intelligence and machine learning (AI/ML) are now being used to optimize material synthesis and manufacturing. Manufacturing automation requires online monitoring and advanced unit operation control with feedback loop learning. Using Bayesian and statistical methods enables logic-derived design and regression analysis into an otherwise trial-and-error approach. Organic and polymer synthesis rely on carefully designed homogenous/heterogenous reaction control beyond batch processes. Additive manufacturing (AM) is important in fabricating highly complex parts and high-performance parts and objects for a digital economy. We will describe in this talk how we have targeted the use of continuous flow reactions under unit operation and the use of large language models (LLM)s, toward an autonomous design and synthesis of polymer materials. A hierarchical approach and learning with control of P, V, T, and flow rate enables new methods of copolymerization and control of kinetics for optimized macromolecular properties. We also discuss the use of AI/ML in nanocomposite 3D printing for highly improved properties in a relatively short time. With AI/ML, it is possible to optimize the characterization method workflows to generate better empirical results that can subjected to further data mining. The next stop is autonomous systems.