Material dynamics analysis with deep generative model

Abstract

Understanding nanoscale material evolution—including phase transitions, structural deformations, and chemical reactions—under dynamic conditions remains a fundamental challenge in materials science. While advanced imaging techniques enable visualization of transformation processes, they typically capture only discrete temporal observations at specific time intervals. Consequently, intermediate stages and alternative pathways between captured images often remain unresolved, introducing ambiguity in analyzing material dynamics and transformation mechanisms. To address these limitations, we present a two-stage framework using deep generative models to probabilistically reconstruct intermediate transformations. Our framework is based on the hypothesis that generative models trained to reproduce experimental images inherently capture the dynamical processes that generated those observations. By integrating these trained generative models into Monte Carlo simulations, we generate plausible transformation pathways that interpolate unobserved intermediate stages. This approach enables the extraction of meaningful insights and the statistical analysis of material dynamics. This study also evaluates the framework's applicability across three phenomena: tantalum test chart translation, gold nanoparticle diffusion in polyvinyl alcohol solution, and copper sulfidation in heterogeneous rubber/brass composites. The generated transformations closely replicate experimental observations while revealing previously unrecognized dynamic behaviors for future experimental validation. These findings suggest that learned generative models encode physically meaningful continuity, enabling statistical interpolation of unobserved intermediate states and classification of transformation modes under sparse observational constraints.