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.

翻译：提出了一种机器学习方法，用于加速在大尺度域中长时程下嵌段共聚物形态演化的计算。该策略利用单体尺度粗粒度粒子演化与介观尺度缓慢形态演化之间的特征时间分离。与经验连续介质模型不同，所提出的方法直接从基于粒子的模拟中学习随机驱动的缺陷湮灭过程。采用了一种能适配不同边界条件的UNet架构，从而允许周期性边界及任意形状的固定基底边界条件。通过损失函数引入物理概念，并通过数据增强融入对称性。利用三种不同用例对该模型进行了验证。应用可解释人工智能方法可视化形态随时间的演化。该方法能够生成大系统尺寸和长轨迹，用于研究不同约束条件下缺陷密度及其演化。作为应用实例，我们展示了获取后期形态对于理解单个嵌段内粒子扩散的重要性。该工作对微电子、电池材料和膜材料中的定向自组装及材料设计具有指导意义。