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

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...

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Bibliographic Details
Published in:npj computational materials 2019-03, Vol.5 (1), Article 39
Main Authors: Suzuki, Yuta, Hino, Hideitsu, Kotsugi, Masato, Ono, Kanta
Format: Article
Language:English
Subjects:
639/301/930/1032
639/301/930/12
Automation
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Correlation analysis
Correlation coefficients
Data analysis
Data processing
Electron energy
Informatics
Kernel functions
Material properties
Materials information
Materials Science
Materials selection
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Parameter estimation
Regression analysis
Regression models
Robustness (mathematics)
Similarity
Similarity measures
Spectra
Theoretical
X ray absorption
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Summary: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.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-019-0176-1