|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Polycrystalline Materials
||Ryan Cohn, Elizabeth Holm
|On-Site Speaker (Planned)
Abnormal grain growth (AGG) significantly influences the properties of materials but is difficult to observe experimentally. Researchers have replicated this phenomenon through Monte Carlo simulations, but have not generated a predictive model that can infer which initial microstructures will undergo AGG during simulated processing. One challenge with modeling grain structures is determining an effective representation to use as an input to a model. Neural message passing allows models to operate on irregular graph structures, which are useful for modeling networks of connected grains but incompatible with traditional neural network architectures. In this study, we apply neural message passing to predicting the occurrence of AGG in Monte Carlo simulations using only the initial state of the system as input. Preliminary results indicate that even a simple graph-based model achieves 75% prediction accuracy and outperforms a comparable computer vision approach.
||Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation