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If variety is the spice of life, then abnormal grain growth (AGG) may be the materials processing equivalent of sriracha sauce. Abnormally growing grains can be prismatic, dendritic, or practically any shape in between. When they grow at least an order of magnitude larger than their neighbors in the matrix—a state we call extreme AGG—we can examine the abnormal/matrix interface for clues to the underlying mechanism. Simulating AGG for various formulations of the grain boundary (GB) equation of motion, we show that anisotropies in GB mobility and energy leave a characteristic fingerprint in the abnormal/matrix boundary. Except in the case of prismatic growth, the morphological signature of most reported instances of AGG is consistent with a certain degree of GB mobility variability. Open questions remain, however, concerning the mechanism by which the corresponding growth advantage is established and maintained as the GBs of abnormal grains advance through the matrix. Expected final online publication date for the Annual Review of Materials Research, Volume 53 is July 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 

Computer vision and machine learning systems for microstructural characterization and analysis are used for a variety of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation, leading to accurate, autonomous, objective, repeatable results in an indefatigable and permanently available manner.