Feature space of XRD patterns constructed by auto-encorder

TL;DR

This paper introduces an auto-encoder based method to identify and quantify the relevance of peaks in XRD patterns for material characterization, revealing non-obvious peak importance.

Contribution

The authors develop a novel auto-encoder scheme to construct a feature space for XRD patterns, enabling quantitative relevancy analysis of individual peaks.

Findings

Identified peaks with high intensity but low relevance.

Revealed peaks not easily explained by physical models.

Demonstrated the scheme's ability to quantify peak importance.

Abstract

It would be a natural expectation that only major peaks, not all of them, would make an important contribution to the characterization of the XRD pattern. We developed a scheme that can identify which peaks are relavant to what extent by using auto-encoder technique to construct a feature space for the XRD peak patterns. Individual XRD patterns are projected onto a single point in the two-dimensional feature space constructed using the method. If the point is significantly shifted when a peak of interest is masked, then we can say the peak is relevant for the characterization represented by the point on the space. In this way, we can formulate the relevancy quantitatively. By using this scheme, we actually found such a peak with a significant peak intensity but low relevancy in the characterization of the structure. The peak is not easily explained by the physical viewpoint such as the higher-order peaks from the same plane index, being a heuristic finding by the power of machine-learning.