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Title: Automated generation and ensemble-learned matching of X-ray absorption spectra

Abstract

X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. Here, we will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures.more » The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [3];  [4];  [4];  [4];  [5];  [2]; ORCiD logo [1]
  1. Univ. of California, San Diego, CA (United States). Dept. of NanoEngineering
  2. Univ. of California, Berkeley, CA (United States). Dept. of Materials Science
  3. National Inst. for Occupational Safety and Health, Centers for Disease Control, Cincinnati, OH (United States). Div. of Applied Research and Technology
  4. Univ. of Washington, Seattle, WA (United States). Dept. of Physics
  5. Binghamton Univ., NY (United States). Dept. of Physics, Applied Physics and Astronomy and Materials Science and Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1490242
Alternate Identifier(s):
OSTI ID: 1559168
Grant/Contract Number:  
FG02-97ER45623; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Zheng, Chen, Mathew, Kiran, Chen, Chi, Chen, Yiming, Tang, Hanmei, Dozier, Alan, Kas, Joshua J., Vila, Fernando D., Rehr, John J., Piper, Louis F. J., Persson, Kristin A., and Ong, Shyue Ping. Automated generation and ensemble-learned matching of X-ray absorption spectra. United States: N. p., 2018. Web. doi:10.1038/s41524-018-0067-x.
Zheng, Chen, Mathew, Kiran, Chen, Chi, Chen, Yiming, Tang, Hanmei, Dozier, Alan, Kas, Joshua J., Vila, Fernando D., Rehr, John J., Piper, Louis F. J., Persson, Kristin A., & Ong, Shyue Ping. Automated generation and ensemble-learned matching of X-ray absorption spectra. United States. doi:10.1038/s41524-018-0067-x.
Zheng, Chen, Mathew, Kiran, Chen, Chi, Chen, Yiming, Tang, Hanmei, Dozier, Alan, Kas, Joshua J., Vila, Fernando D., Rehr, John J., Piper, Louis F. J., Persson, Kristin A., and Ong, Shyue Ping. Tue . "Automated generation and ensemble-learned matching of X-ray absorption spectra". United States. doi:10.1038/s41524-018-0067-x. https://www.osti.gov/servlets/purl/1490242.
@article{osti_1490242,
title = {Automated generation and ensemble-learned matching of X-ray absorption spectra},
author = {Zheng, Chen and Mathew, Kiran and Chen, Chi and Chen, Yiming and Tang, Hanmei and Dozier, Alan and Kas, Joshua J. and Vila, Fernando D. and Rehr, John J. and Piper, Louis F. J. and Persson, Kristin A. and Ong, Shyue Ping},
abstractNote = {X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. Here, we will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.},
doi = {10.1038/s41524-018-0067-x},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United States},
year = {2018},
month = {3}
}

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Works referenced in this record:

Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
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