Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
Abstract
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.
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division
- OSTI Identifier:
- 1593265
- Alternate Identifier(s):
- OSTI ID: 1571355
- Report Number(s):
- BNL-213542-2020-JAAM
Journal ID: ISSN 0021-9606; TRN: US2100792
- Grant/Contract Number:
- SC0012704; FG02-03ER15476; AC02-06CH11357; AC-02-06CH11357; 20202
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 151; Journal Issue: 16; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY
Citation Formats
Liu, Yang, Marcella, Nicholas, Timoshenko, Janis, Halder, Avik, Yang, Bing, Kolipaka, Lakshmi, Pellin, Michael. J., Seifert, Soenke, Vajda, Stefan, Liu, Ping, and Frenkel, Anatoly I. Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning. United States: N. p., 2019.
Web. doi:10.1063/1.5126597.
Liu, Yang, Marcella, Nicholas, Timoshenko, Janis, Halder, Avik, Yang, Bing, Kolipaka, Lakshmi, Pellin, Michael. J., Seifert, Soenke, Vajda, Stefan, Liu, Ping, & Frenkel, Anatoly I. Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning. United States. https://doi.org/10.1063/1.5126597
Liu, Yang, Marcella, Nicholas, Timoshenko, Janis, Halder, Avik, Yang, Bing, Kolipaka, Lakshmi, Pellin, Michael. J., Seifert, Soenke, Vajda, Stefan, Liu, Ping, and Frenkel, Anatoly I. 2019.
"Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning". United States. https://doi.org/10.1063/1.5126597. https://www.osti.gov/servlets/purl/1593265.
@article{osti_1593265,
title = {Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning},
author = {Liu, Yang and Marcella, Nicholas and Timoshenko, Janis and Halder, Avik and Yang, Bing and Kolipaka, Lakshmi and Pellin, Michael. J. and Seifert, Soenke and Vajda, Stefan and Liu, Ping and Frenkel, Anatoly I.},
abstractNote = {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.},
doi = {10.1063/1.5126597},
url = {https://www.osti.gov/biblio/1593265},
journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = 16,
volume = 151,
place = {United States},
year = {Tue Oct 22 00:00:00 EDT 2019},
month = {Tue Oct 22 00:00:00 EDT 2019}
}
Web of Science
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