Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Yuta Suzuki, Hideitsu Hino, Masato Kotsugi, Kanta Ono

ABSTRACT

Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard, spectroscopic data are widely used for materials discovery because these data include essential information about materials. An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure, or kernel function. The required measure should be robust in terms of peak shifting, peak broadening, and noise. However, the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved. We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra. The similarity measures show good correspondence with the materials parameter, that is, the crystal-field parameter, in all measures. The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening. We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra. The regression model enabled the materials parameter, that is, 10 Dq, to be automatically estimated from the spectra. With regard to research progress in similarity measures, this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data. More... »

PAGES

39

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URI

http://scigraph.springernature.com/pub.10.1038/s41524-019-0176-1

DOI

http://dx.doi.org/10.1038/s41524-019-0176-1

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https://app.dimensions.ai/details/publication/pub.1113061285


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