(500a) At the Nexus of Simulation and Machine Learning for Sequence Design of Polymers

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 explore the complementary roles of simulation (ranging from quantum chemical computation to coarse-grained modeling) and data science in sequence design of polymers. Examples will range from assessing single-chain polymer conformation to tailoring interactions with biological compounds. These examples will highlight the need for new developments in both the machine learning and simulation space to enhance polymer design.