Machine learning-assisted close-set X-ray diffraction phase identification of transition metals

Maksim Zhdanov, Andrey Zhdanov

Keywords: machine learning, material science, x-ray diffraction, phase identification

TL;DR: Close-set XRD phase identification of transtion metals based on statistical properties and machine learning

Abstract: Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to predict crystal structure phases from X-ray diffraction data of transition metals and their oxides. We evaluate the performance of our method and compare the variety of its settings. Our results demonstrate that the proposed machine learning framework achieves competitive performance. This demonstrates the potential for machine learning to significantly impact the field of X-ray diffraction and crystal structure determination.