Cohn Ryan, Holm Elizabeth A
Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA.
Department of Materials Science and Engineering, University of Michigan, 500 S State St, Ann Arbor, MI, USA.
Sci Rep. 2024 Dec 4;14(1):30259. doi: 10.1038/s41598-024-81349-3.
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate a large dataset of Monte Carlo simulations of abnormal grain growth. We train simple graph convolution networks to predict which initial microstructures will exhibit abnormal grain growth, and compare the results to a standard computer vision approach for the same task. The graph neural network outperformed the computer vision method and achieved 73% prediction accuracy and fewer false positives. It also provided some physical insight into feature importance and the relevant length scale required to maximize predictive performance. Analysis of the uncertainty in the Monte Carlo simulations provides additional insights for ongoing work in this area.
图神经网络的最新进展显示出预测异常晶粒生长发生情况的前景，由于其明显的随机性，这一直是一个极具挑战性的研究领域。在本研究中，我们生成了一个关于异常晶粒生长的蒙特卡罗模拟的大型数据集。我们训练简单的图卷积网络来预测哪些初始微观结构会出现异常晶粒生长，并将结果与用于同一任务的标准计算机视觉方法进行比较。图神经网络的表现优于计算机视觉方法，实现了73%的预测准确率且误报更少。它还为特征重要性以及最大化预测性能所需的相关长度尺度提供了一些物理见解。对蒙特卡罗模拟中的不确定性进行分析，为该领域正在进行的工作提供了更多见解。
