@article{Zheng:nb5377, author = "Zheng, Weijian and Park, Jun-Sang and Kenesei, Peter and Ali, Ahsan and Liu, Zhengchun and Foster, Ian and Schwarz, Nicholas and Kettimuthu, Rajkumar and Miceli, Antonino and Sharma, Hemant", title = "{Rapid detection of rare events from {\it in situ} X-ray diffraction data using machine learning}", journal = "Journal of Applied Crystallography", year = "2024", volume = "57", number = "4", pages = "1158--1170", month = "Aug", doi = {10.1107/S160057672400517X}, url = {https://doi.org/10.1107/S160057672400517X}, abstract = {High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ({\it e.g.} peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.}, keywords = {high-energy diffraction microscopy, machine learning}, }