Accelerating Discovery and Design for Thin Film Polymer Self-Assembly through Machine Learning

Thin film polymer self-assembly is a powerful approach to create nanoscale patterns across large areas with low cost for diverse applications in electronics and materials science. The confluence of compositional and architectural variation, entropic and surface energy constraints arising from thin film confinement, and process-dependent assembly pathways yields of panoply of nanoscale morphologies of which only a fraction can be discovered, catalogued, and engineered through manual experimentation. This talk will elaborate examples where artificial intelligence and machine learning (AI/ML) can accelerate thin film polymer self-assembly research. We describe the application of Bayesian optimization to automate x-ray scattering characterization for the discovery of new morphologies in template-directed self-assembly of block copolymer blends. We then illustrate the use of foundation model-supported "few shot" image classification for determining processing-morphology relationships in spray-deposited block copolymer/homopolymer blend films with minimal quantities of data. The potential for transfer learning from image classification for selecting high value but time-intensive in situ x-ray characterization experiments will also be discussed.