The self-assembly
of soft matter provides a practical and scalable
route toward the production of nanostructured materials, with minimal
need for direct intervention at nanoscopic length scales. Symmetric
diblock copolymers, which can self-assemble into a lamellar phase,
are a prototype for this class of materials. In this work, we introduce
a machine learning model that is trained by intermediate time-scale
simulations of a soft, coarse-grained model. The aim of the model
is to simulate defect kinetics in the lamellar morphology, as the
material relaxes toward equilibrium. To do so, we exploit the physical
characteristics of overdamped dynamics and formulate the problem of
time evolution as a Markov chain. The trained artificial neural network
(ANN) predicts a time-independent transition probability from one
time step to the next. As a result, we arrive at a method that can
be repeatedly applied to generate long-time trajectories. Predicting
defect kinetics in this manner provides hitherto unavailable insights
into the late-time dynamics of block copolymer relaxation. The neural
network is purposely designed to be independent of input size, which
enables training on small systems, and enabling predictions over large
scales. As a demonstration of these capabilities, in this work, we
leverage the ANN to obtain information about the statistics of defect
motion and lifetimes over a long-range ordering process.