Inverse Design of Complex Block Copolymers for Exotic Self-Assembled Structures Based on Bayesian Optimization

Variable
chain topologies of multiblock copolymers provide
great
opportunities for the formation of numerous self-assembled nanostructures
with promising potential applications. However, the consequent large
parameter space poses new challenges for searching the stable parameter
region of desired novel structures. In this Letter, by combining Bayesian
optimization (BO), fast Fourier transform-assisted 3D convolutional
neural network (FFT-3DCNN), and self-consistent field theory (SCFT),
we develop a data-driven and fully automated inverse design framework
to search for the desired novel structures self-assembled by ABC-type
multiblock copolymers. Stable phase regions of three exotic target
structures are efficiently identified in high-dimensional parameter
space. Our work advances the new research paradigm of inverse design
in the field of block copolymers.