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Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
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SYSNO ASEP 0510111 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning Author(s) Liu, Y. (US)
Marcella, N. (US)
Timoshenko, J. (US)
Halder, A. (US)
Yang, B. (US)
Kolipaka, L. (US)
Pellin, M. J. (US)
Seifert, S. (US)
Vajda, Štefan (UFCH-W) RID, ORCID
Liu, P. (US)
Frenkel, A. I. (US)Article number 164201 Source Title Journal of Chemical Physics. - : AIP Publishing - ISSN 0021-9606
Roč. 151, č. 16 (2019)Number of pages 7 s. Language eng - English Country US - United States Keywords XANES spectra ; Copper alloys ; metal ions Subject RIV CF - Physical ; Theoretical Chemistry OECD category Physical chemistry Method of publishing Limited access Institutional support UFCH-W - RVO:61388955 UT WOS 000500362000031 EID SCOPUS 85074148488 DOI 10.1063/1.5126597 Annotation 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. Workplace J. Heyrovsky Institute of Physical Chemistry Contact Michaela Knapová, michaela.knapova@jh-inst.cas.cz, Tel.: 266 053 196 Year of Publishing 2020 Electronic address http://hdl.handle.net/11104/0300663
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