0510111 - ÚFCH JH 2020 RIV US eng J - Článek v odborném periodiku
Liu, Y. - Marcella, N. - Timoshenko, J. - Halder, A. - Yang, B. - Kolipaka, L. - Pellin, M. J. - Seifert, S. - Vajda, Štefan - Liu, P. - Frenkel, A. I.
Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning.
Journal of Chemical Physics. Roč. 151, č. 16 (2019), č. článku 164201. ISSN 0021-9606. E-ISSN 1089-7690
GRANT EU: European Commission(XE) 810310
Institucionální podpora: RVO:61388955
Klíčová slova: XANES spectra * Copper alloys * metal ions
Obor OECD: Physical chemistry
Impakt faktor: 2.991, rok: 2019 ; AIS: 0.761, rok: 2019
Způsob publikování: Omezený přístup
DOI: https://doi.org/10.1063/1.5126597

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.
Trvalý link: http://hdl.handle.net/11104/0300663