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. 2018 Jul 31:5:180151.
doi: 10.1038/sdata.2018.151.

High-throughput computational X-ray absorption spectroscopy

Kiran Mathew  1 , Chen Zheng  2 , Donald Winston  3 , Chi Chen  2 , Alan Dozier  4 , John J Rehr  5 , Shyue Ping Ong  2 , Kristin A Persson  1
Affiliations

High-throughput computational X-ray absorption spectroscopy

Kiran Mathew et al. Sci Data. .

Abstract

X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique. In this work we present a database of computed XAS spectra, using the Green's formulation of the multiple scattering theory implemented in the FEFF code. With more than 500,000 K-edge X-ray absorption near edge (XANES) spectra for more than 40,000 unique materials, this database constitutes the largest existing collection of computed XAS spectra to date. The data is openly distributed via the Materials Project, enabling researchers across the world to access it for free and use it for comparisons with experiments and further analysis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1. Schematic diagram of the high throughput framework employed in the generation of XAS spectra for the Materials Project.
Figure 2
Figure 2. Screen shot of XAS Matcher web application.
The web application is hosted at https://materialsproject.org/#apps/xas.
Figure 3
Figure 3. Benchmarking results of rfms1 parameter in the SCF card for K-edge XANES of various materials.
Pearson correlation coefficients were calculated between spectra calculated at different rfms1 and the experimental reference provided by electron energy-loss spectroscopy (EELS) Data Base.
Figure 4
Figure 4. Sample comparisons of FEFF computed K edge XANES spectra with the corresponding experimental ones for six different compounds.
Computational spectra are shifted upwards by 0.5. (a) LiCoO2, (b) LiNiO2, (c) NaCl, (d) V2O5, (e) VO2, (f) V2O3.
See this image and copyright information in PMC

References

Data Citations

    1. Mathew K., et al. . 2018. Figshare. https://doi.org/10.6084/m9.figshare.c.3946561 - DOI

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