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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Discovering New Mechanisms of Grain Growth with a Machine Learning Model Trained on Experimental and Simulation Data
Author(s)	Michael R. Tonks, Amanda Krause, Joel Harley, Lin Yang, Vishal Yadav, Joseph Melville, Bryan Conry
On-Site Speaker (Planned)	Michael R. Tonks
Abstract Scope	Current grain growth models fail to fully explain the 4D grain growth phenomena revealed by non-destructive 3D x-ray diffraction microscopy (3D-XRM). To address this, we have developed the physics-regularized interpretable machine learning microstructure evolution (PRIMME) model, capable of accurately predicting both isotropic and anisotropic grain growth in 2D and 3D. Regularization guided by physical laws enhances the model's reliability and some data is provided to help interpret the model predictions. Initially trained on phase field and Monte Carlo Potts simulated data, PRIMME has now been extended to incorporate experimental 3D-XRM microstructure data. Preliminary validation shows superior performance in capturing complex grain dynamics compared to traditional models. Ongoing training efforts aim to refine our understanding of grain growth, with potential implications for materials design and industrial processing.
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, Machine Learning,

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