Using Particle Swarm Optimization and SCFT to agnostically identify the stable and low-lying metastable competitive morphologies of block copolymers.*

The unguided search for the stable phase of a block copolymer of a given composition and architecture is a problem of global optimization with important ramifications from a materials discovery perspective. We discuss the development of a reciprocal-space Particle Swarm Optimization (PSO)-SCFT method in which Fourier components of SCFT fields near the principal shell are manipulated. Effectively, PSO-SCFT facilitates the search through a space of reciprocal-space SCFT seeds which yield a variety of morphologies. Using intensive free energy as a fitness metric by which to compare these morphologies, the PSO-SCFT methodology allows us to agnostically identify low-lying competitive and stable morphologies. In this talk, we present results for applying PSO-SCFT to conformationally symmetric diblock copolymers and miktoarm star polymers, and discuss the successes and challenges of the method.