Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning

Liu, Yang; Marcella, Nicholas; Timoshenko, Janis; Halder, Avik; Yang, Bing; Kolipaka, Lakshmi; Pellin, Michael. J.; Seifert, Soenke; Vajda, Stefan; Liu, Ping; Frenkel, Anatoly I.

doi:10.1063/1.5126597

Title: Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning

Journal Article · Tue Oct 22 00:00:00 EDT 2019 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.5126597· OSTI ID:1593265

Liu, Yang ^[1]; Marcella, Nicholas ^[1];

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^[2]; Kolipaka, Lakshmi ^[2];

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Stony Brook Univ., NY (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States); Czech Academy of Sciences (Prague & Czech Republic)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

Understanding the origins of enhanced reactivity of supported, subnanometer in size, metal oxide clusters is challenging due to the scarcity of methods capable to extract atomic-level information from the experimental data. Due to both the sensitivity of X-ray absorption near edge structure (XANES) spectroscopy to the local geometry around metal ions and reliability of theoretical spectroscopy codes for modeling XANES spectra, supervised machine learning approach has become a powerful tool for extracting structural information from the experimental spectra. Here, we present the application of this method to grazing incidence XANES spectra of size-selective Cu oxide clusters on flat support, measured in operando conditions of the methanation reaction. We demonstrate that the convolution neural network can be trained on theoretical spectra and utilized to “invert” experimental XANES data to obtain structural descriptors—the Cu–Cu coordination numbers. As a result, we were able to distinguish between different structural motifs (Cu₂O-like and CuO-like) of Cu oxide clusters, transforming in reaction conditions, and reliably evaluate average cluster sizes, with important implications for the understanding of structure, composition, and function relationships in catalysis.

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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). Chemical Sciences, Geosciences, and Biosciences Division

Grant/Contract Number:: SC0012704; FG02-03ER15476; AC02-06CH11357; AC-02-06CH11357; 20202

OSTI ID:: 1593265

Alternate ID(s):: OSTI ID: 1571355

Report Number(s):: BNL-213542-2020-JAAM; TRN: US2100792

Journal Information:: Journal of Chemical Physics, Vol. 151, Issue 16; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 33 works

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