(107c) Navigating and Sculpting Structure-Function Landscapes of Polymeric Materials

The chemical design of polymers with target structural and/or functional properties represents a grand challenge 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 molecular simulation, machine learning, and, in some cases, experimental data to navigate complex polymer design spaces in search of specific properties. I will specifically share examples related to the structure/mechanics of single-chain nanoparticles, composition-dependent phase behavior of polymer solutions, and the interactivity of polymer chains with biological interfaces. These vignettes will highlight both methodological advancements as well as intriguing applications for machine learning and simulation of soft materials.