(255c) AI-Driven Experiments and Open-Source Automation for Accelerated Soft Matter Research

Artificial intelligence (AI), when paired with accessible laboratory automation, can greatly accelerate materials optimization and scientific discovery. For example, it can be used to efficiently map a phase-diagram with intelligent sampling along phase boundaries, or in ‘retrosynthesis’ problems where a material with a target structure is desired but a synthetic route is not known. These approaches are especially promising in soft matter systems, including block copolymer self-assembly, nanoparticle synthesis, and controlled colloidal assembly. In these systems, design parameters (e.g. chemical composition, MW, topology, processing) are vast, history-dependent metastable and ‘out-of-equilibrium’ structures are common, and functional properties are intimately tied to molecular design features and processing conditions. In addition, for AI algorithms to operate efficiently in these spaces, they must be ‘encoded’ with domain expertise specific to the problems being tackled. This talk will cover recent advances in hardware and software tools for accelerated materials research in polymeric and soft-matter systems including dispersions and colloids. Finally, it will outline remaining challenges in practical implementations and identify future opportunities for research.