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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 Assessing Uncertainty and Reproducibility in ML-Based Grain Growth Predictions: Impact of Training Data
Author(s) Vishal Yadav, Amanda Krause, Joel Harley, Michael Tonks
On-Site Speaker (Planned) Vishal Yadav
Abstract Scope Machine learning (ML) based models for polycrystalline microstructure evolution have emerged as promising tools, demonstrating their ability to model both isotropic and anisotropic grain growth. However, the effects of uncertainties in model architecture, extrapolation to larger times and domain sizes, and training data variability on the stability, accuracy, and reproducibility of ML-based grain growth predictions remain unclear. This study investigates these effects using two training datasets generated by Monte Carlo Potts (MCP) and phase-field (PF) simulations with the PRIMME (Physics-Regularized Interpretable Machine Learning of Microstructure Evolution) framework. Results show substantial variation in MCP-trained PRIMME simulation outcomes despite identical training data. In contrast, PF-trained PRIMME simulations exhibited relatively less variation, though some uncertainty persisted. These insights provide valuable guidance for optimizing PRIMME model performance when training with experimental microstructure data.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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