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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 Learning topological evolution in polycrystalline microstructures
Author(s) Ashwin Shahani
On-Site Speaker (Planned) Ashwin Shahani
Abstract Scope Predicting the evolution of polycrystalline microstructures remains a central challenge in materials science because grain boundaries (GBs) interact through complex three-dimensional networks whose collective behavior is difficult to capture with existing theories. Here, we present a graph neural network trained directly on four-dimensional (three-dimensional space- and time-resolved) synchrotron diffraction-contrast X-ray tomography data comprising more than 20,000 experimentally observed GBs. The model predicts whether individual GBs persist, form, or disappear during annealing with over 84% accuracy and generalizes successfully to independent datasets acquired under different processing conditions. By interrogating the trained network, we identify the structural descriptors that govern GB evolution and demonstrate that the fate of a boundary depends strongly on its surrounding network rather than on its local properties alone. The data reveal where current models fail to capture experimentally observed behavior, providing new insight into the mechanisms governing microstructural evolution and pathways toward more predictive mesoscale theories.
Proceedings Inclusion? Planned:
Keywords Characterization, Machine Learning, Modeling and Simulation

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