Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution

Abnormal grain growth can significantly alter the properties of materials during processing. This can cause significant variation in the properties and performance of in-spec feedstock components subjected to identical processing paths. Understanding and controlling abnormal grain growth has proved to be elusive due to the stochastic nature of this phenomenon. However, recent advances in deep learning provide a promising alternative to traditional experimental and physics-based methods for understanding this phenomenon. Neural message passing allows deep learning to be applied to irregular inputs including graph representations of grain structures in a material. In this study we generate a large database of Monte Carlo simulations of abnormal grain growth in an idealized system. We apply message passing neural networks to predict the occurrence of abnormal grain growth in these simulations using only the initial state of the system as input. A computer vision model is also trained for the same task for comparison. The preliminary results indicate that the message passing approach outperforms the computer vision method and achieved 75% prediction accuracy, significantly better than random guessing. Analysis of the uncertainty in the Monte Carlo simulations provides a road map for ongoing work on this project.