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Title: Classification of local chemical environments from x-ray absorption spectra using supervised machine learning

Abstract

We report that x-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry, and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semiquantitative and not transferable. In this paper, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition-metal families. Lastly, we found that spectral features beyond the preedge region play an important role in the local structure classification problem especially for the late 3d transition-metal elements.

Authors:
 [1];  [2];  [2]; ORCiD logo [2]
  1. Columbia Univ., New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1501594
Alternate Identifier(s):
OSTI ID: 1546341
Report Number(s):
BNL-211387-2019-JAAM
Journal ID: ISSN 2475-9953; PRMHAR
Grant/Contract Number:  
SC0012704; FG02-97ER25308; 16-039
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 3; Journal Issue: 3; Journal ID: ISSN 2475-9953
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Carbone, Matthew R., Yoo, Shinjae, Topsakal, Mehmet, and Lu, Deyu. Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. United States: N. p., 2019. Web. doi:10.1103/PhysRevMaterials.3.033604.
Carbone, Matthew R., Yoo, Shinjae, Topsakal, Mehmet, & Lu, Deyu. Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. United States. doi:10.1103/PhysRevMaterials.3.033604.
Carbone, Matthew R., Yoo, Shinjae, Topsakal, Mehmet, and Lu, Deyu. Wed . "Classification of local chemical environments from x-ray absorption spectra using supervised machine learning". United States. doi:10.1103/PhysRevMaterials.3.033604.
@article{osti_1501594,
title = {Classification of local chemical environments from x-ray absorption spectra using supervised machine learning},
author = {Carbone, Matthew R. and Yoo, Shinjae and Topsakal, Mehmet and Lu, Deyu},
abstractNote = {We report that x-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry, and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semiquantitative and not transferable. In this paper, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition-metal families. Lastly, we found that spectral features beyond the preedge region play an important role in the local structure classification problem especially for the late 3d transition-metal elements.},
doi = {10.1103/PhysRevMaterials.3.033604},
journal = {Physical Review Materials},
number = 3,
volume = 3,
place = {United States},
year = {2019},
month = {3}
}

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Works referenced in this record:

Battery materials for ultrafast charging and discharging
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  • Kang, Byoungwoo; Ceder, Gerbrand
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  • DOI: 10.1038/nature07853