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:
-
[2]
- Columbia Univ., New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
- 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)
- 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. https://doi.org/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. https://doi.org/10.1103/PhysRevMaterials.3.033604. https://www.osti.gov/servlets/purl/1501594.
@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 = {Wed Mar 13 00:00:00 EDT 2019},
month = {Wed Mar 13 00:00:00 EDT 2019}
}
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