Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F19%3A00510111" target="_blank" >RIV/61388955:_____/19:00510111 - isvavai.cz</a>
Result on the web
<a href="http://hdl.handle.net/11104/0300663" target="_blank" >http://hdl.handle.net/11104/0300663</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.5126597" target="_blank" >10.1063/1.5126597</a>
Alternative languages
Result language
angličtina
Original language name
Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Chemical Physics
ISSN
0021-9606
e-ISSN
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Volume of the periodical
151
Issue of the periodical within the volume
16
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
164201
UT code for WoS article
000500362000031
EID of the result in the Scopus database
2-s2.0-85074148488