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

Carbone, Matthew R.; Yoo, Shinjae; Topsakal, Mehmet; Lu, Deyu

doi:10.1103/PhysRevMaterials.3.033604

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

Journal Article · Wed Mar 13 00:00:00 EDT 2019 · Physical Review Materials

DOI:https://doi.org/10.1103/PhysRevMaterials.3.033604· OSTI ID:1501594

Carbone, Matthew R. ^[1]; Yoo, Shinjae ^[2]; Topsakal, Mehmet ^[2];

^[2]

Columbia Univ., New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)

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.

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Research Organization:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0012704; FG02-97ER25308; 16-039

OSTI ID:: 1501594

Alternate ID(s):: OSTI ID: 1546341

Report Number(s):: BNL-211387-2019-JAAM; PRMHAR

Journal Information:: Physical Review Materials, Vol. 3, Issue 3; ISSN 2475-9953

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 47 works

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