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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Self-Supervised Learning of Materials States Evolution from Microscopy Data Using Joint Embedding Predictive Architectures
Author(s) Hyoungryul Park, Mathieu Calvat, Cornor Bergan, Dhruv Anjaria
On-Site Speaker (Planned) Hyoungryul Park
Abstract Scope Modern microscopy generates vast amounts of spatially resolved information describing material evolution during deformation, oxidation, recrystallization, and other thermomechanical processes. However, extracting the underlying physical mechanisms and associated features remains largely dependent on manual interpretation or supervised learning requiring extensive annotations. Here, we investigate Joint Embedding Predictive Architectures (JEPA) as a self-supervised framework for learning physically meaningful representations directly from multimodal microscopy data. By predicting latent representations rather than image pixels, JEPA learns the evolution of material states without manual labels. We evaluate whether these latent embeddings capture physically relevant microstructural, oxidation and deformation mechanisms and demonstrate that they can be decoded into accurate semantic segmentations of materials features. This work establishes a pathway toward autonomous, physics-aware microscopy, enabling artificial intelligence to discover and characterize materials mechanisms directly from experimental observations while substantially reducing the need for expert annotation.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, Other

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