Genetic Algorithm for Discovery of Globally Stable Phases in Block Copolymers

A method of determining the globally stable morphologies of block copolymers using a framework composed of a genetic algorithm (GA) in conjunction with self-consistent field theory (SCFT) is described. We present results from benchmark testing of this GA-SCFT technique on the canonical AB diblock copolymer melt, for which the theoretical phase diagram has long been established. The GA-SCFT algorithm successfully predicts many of the conventional mesophases from random initial conditions in large, three-dimensional simulation cells, including HEX, LAM, and BCC, over a broad composition range and weak to moderate segregation strength. However, the GA-SCFT method is currently not effective at recovery of network phases, such as the gyroid structure, or for the BCC phase in the strong segregation limit. These challenges may be partially attributed to the large density of defective states seen in large simulation cells and at higher segregation strengths.