Data-driven Strategies to Navigate Sequence, Composition, and Architectural Complexity in Polymer Physics

Understanding and designing polymers with target structural and/or functional properties are grand challenges in materials science. Although artificial intelligence and machine learning have greatly enhanced design efforts for many materials classes, applications to polymer systems remains limited, particularly those aiming to exploit property variation due to composition, sequence, or topological effects. In this talk, I will describe our efforts to combine simulation, machine learning, and concepts of polymer physics to navigate complex polymer design spaces and accurately construct structure-function relationships in diverse applications. I will specifically share examples related to the structure/mechanics of single-chain nanoparticles, the rheology of complex polymer solutions, and the interactivity of polymer chains with biological interfaces. These vignettes will highlight both methodological advancements as well as intriguing application areas.