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Recent developments in graph neural networks show promise for predicting theoccurrence of abnormal grain growth, which has been a particularly challengingarea of research due to its apparent stochastic nature. In this study, wegenerate a large dataset of Monte Carlo simulations of abnormal grain growth.We train simple graph convolution networks to predict which initialmicrostructures will exhibit abnormal grain growth, and compare the results toa standard computer vision approach for the same task. The graph neural networkoutperformed the computer vision method and achieved 73% prediction accuracyand fewer false positives. It also provided some physical insight into featureimportance and the relevant length scale required to maximize predictiveperformance. Analysis of the uncertainty in the Monte Carlo simulationsprovides additional insights for ongoing work in this area.

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution