Closed-loop, sequential learning for polymer systems

Closed-loop, sequential machine learning has garnered an increasing amount of interest over the past few years in experimental science due to its ability to efficiently explore combinatorially large spaces of experimental parameters. Techniques in this new field can help accelerate scientific exploration of such spaces through the strategic selection of experiments whose outcomes could potentially yield a high amount of information. In this talk, we explore a few applications of such closed-loop, sequential design of experiments in the study of polymer systems, ranging from the optimization of polymer emulsions for use in drug delivery, to recent work in efficient phase mapping of polymerization phenomena and even real-time optimal control in driving block-copolymer evolution. While each example differs in application and experimental objectives, we will present a unified framework for the modeling and decision-making employed in each case. We will also present our work in the development of general-purpose tools designed to lower the barriers for applying these algorithms and techniques to other problems.