Characteristic space of XRD patterns in machine-learning

X-ray diffraction (XRD) is a commonly used analytical technique to identify crystal structures. Recent advances in automatic measuring techniques enable one to obtain thousands of XRD patterns within a day. Basically, however, their analysis is done by comparing measured patterns with reference ones, which relies on experts' knowledge and great efforts. Even using a computational implement of the Rietveld refinement, characterization of some XRD patterns would take a few hours (or more) even for experts. Thus, automation/acceleration of the XRD analysis is really desired for managing high throughput XRD patterns. In this study we adopted an unsupervised machine learning technique, auto-encoder, to analyze XRD patterns. Vectorizing XRD patterns to make feature vectors, the encoder itself maps the high-dimensional vectors onto low-dimensional (say, 2-dim.) ones. It was found that XRD patterns get into groups of different compositions in the reduced feature space. We thus concluded that our scheme can capture slight difference in lattice constant caused by atomic substitutions in magnetic alloys without any prior knowledge.