Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. We compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches. The need for automated processing of coherent diffraction data is strongly motivated by the advent of fourth-generation synchrotron x-ray sources, where coherent diffraction data will be generated at a tremendous rate and human interaction with data analysis, and especially iterative phase retrieval image reconstruction, will become untenable. Our approach provides a path to dealing with this necessary improvement in data processing efficiency. We expect that this work, which demonstrates the applicability of automated machine learning to x-ray analysis, will be of broad interest to scientists and users of synchrotron and XFEL facilities.