  A machine learning approach is presented to accelerate the computation of
block polymer morphology evolution for large domains over long timescales. The
strategy exploits the separation of characteristic times between coarse-grained
particle evolution on the monomer scale and slow morphological evolution over
mesoscopic scales. In contrast to empirical continuum models, the proposed
approach learns stochastically driven defect annihilation processes directly
from particle-based simulations. A UNet architecture that respects different
boundary conditions is adopted, thereby allowing periodic and fixed substrate
boundary conditions of arbitrary shape. Physical concepts are also introduced
via the loss function and symmetries are incorporated via data augmentation.
The model is validated using three different use cases. Explainable artificial
intelligence methods are applied to visualize the morphology evolution over
time. This approach enables the generation of large system sizes and long
trajectories to investigate defect densities and their evolution under
different types of confinement. As an application, we demonstrate the
importance of accessing late-stage morphologies for understanding particle
diffusion inside a single block. This work has implications for directed
self-assembly and materials design in micro-electronics, battery materials, and
membranes.
