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.

用机器学习方法加速计算大域上长时间尺度下的块聚合物形态演化。通过分离粗粒化颗粒演化的特征时间和介观尺度上的缓慢形态演化，利用粒子模拟直接学习随机驱动的缺陷消除过程。采用支持不同边界条件的 UNet 架构，允许任意形状的周期和固定基底边界条件。通过损失函数引入物理概念，数据增强引入对称性，有效性经三个不同的用例验证，可可视化时空演化，研究不同形态限制下的缺陷密度及其演化。在应用方面，展示了访问后期形态对理解单个块内粒子扩散的重要性。对于微电子、电池材料和膜的定向自组装和材料设计有重要意义。