Automated chain architecture screening for discovery of block copolymer assembly with graph enhanced self-consistent field theory

Abstract

The diverse chain architectures of block copolymers makes them important for exploring new self-assembly, but poses significant challenges for identifying the stability windows of desired mesophases within the vast parameter space. Here, we present an automated workflow for screening chain architectures to discover new self-assembly. Utilizing graph-enhanced self-consistent field theory complemented by a scattering-based identification strategy, our approach enables the automated computation of arbitrary chain architectures and their phase behavior. This framework successfully identifies stable windows for a novel PtS phase in AB-type block copolymer melts, with two distinct chain architectures emerging from the screening process. Our findings demonstrate the utility of this method in stabilizing desired self-assembly and exploring new mesophases. The flexibility of our approach allows for straightforward extension to multi-species and multi-component systems and further integration with metaheuristic optimization techniques to enhance its potential for materials design. Block copolymers have diverse chain architectures which self-assemble in many ways makes it difficult to identify the stability windows of the mesophases. Here, an automated workflow using graph-enhanced self-consistent field theory allows for computation of arbitrary chain architectures and their phase behavior.