Rapid detection of rare events from in situ X-ray diffraction data using
  machine learning
Weijian Zheng Jun-Sang Park Peter Kenesei Ahsan Ali Zhengchun Liu Ian T. Foster Nicholas Schwarz Rajkumar Kettimuthu Antonino Miceli Hemant Sharma
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 over time
of the evolving microstructure and attributes. 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. Here we present a fully
automated technique capable of rapidly detecting the onset of plasticity in
high-energy X-ray microscopy data. Our technique is computationally faster by
at least 50 times than the traditional approaches and works for data sets that
are up to 9 times sparser than a full data set. This new technique leverages
self-supervised image representation learning and clustering to transform
massive data into compact, semantic-rich representations of visually salient
characteristics (e.g., peak shapes). These characteristics can be a rapid
indicator of anomalous events such as changes in diffraction peak shapes. We
anticipate that this technique will provide just-in-time actionable information
to drive smarter experiments that effectively deploy multi-modal X-ray
diffraction methods that span many decades of length scales.