Abstract
We present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an X-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the X-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and intrinsically multidimensional (5-dimensional). By a judicious application of established unsupervised machine learning techniques such as k-means clustering and multivariate analysis to this multidimensional data cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions such as lattice tilts and dislocation arrays. We demonstrate this “Big Data” approach to X-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.