Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions
Computational Physics
2020-08-21 v2 Materials Science
Data Analysis, Statistics and Probability
Abstract
We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (around 90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices).
Keywords
Cite
@article{arxiv.1810.03972,
title = {Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions},
author = {Keishu Utimula and Rutchapon Hunkao and Masao Yano and Hiroyuki Kimoto and Kenta Hongo and Shogo Kawaguchi and Sujin Suwanna and Ryo Maezono},
journal= {arXiv preprint arXiv:1810.03972},
year = {2020}
}