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Abnormal grain growth can significantly alter the properties of materialsduring processing. This can cause significant variation in the properties andperformance of in-spec feedstock components subjected to identical processingpaths. Understanding and controlling abnormal grain growth has proved to beelusive due to the stochastic nature of this phenomenon. However, recentadvances in deep learning provide a promising alternative to traditionalexperimental and physics-based methods for understanding this phenomenon.Neural message passing allows deep learning to be applied to irregular inputsincluding graph representations of grain structures in a material. In thisstudy we generate a large database of Monte Carlo simulations of abnormal graingrowth in an idealized system. We apply message passing neural networks topredict the occurrence of abnormal grain growth in these simulations using onlythe initial state of the system as input. A computer vision model is alsotrained for the same task for comparison. The preliminary results indicate thatthe message passing approach outperforms the computer vision method andachieved 75% prediction accuracy, significantly better than random guessing.Analysis of the uncertainty in the Monte Carlo simulations provides a road mapfor ongoing work on this project.

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