Prediction of Diblock Copolymer Morphology via Machine Learning

TL;DR

This paper introduces a machine learning model that predicts the evolution of diblock copolymer morphology efficiently, capturing defect dynamics and enabling large-scale, long-term simulations relevant for materials science applications.

Contribution

It presents a novel UNet-based machine learning approach that learns defect annihilation processes directly from particle simulations, improving prediction speed and scale.

Findings

Accurately predicts morphology evolution over large domains.

Visualizes defect dynamics using explainable AI methods.

Demonstrates relevance for materials design and self-assembly processes.

Abstract

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.