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

Yang Liu1, Nicholas Marcella2, Janis Timoshenko2

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Machine learning deciphers atomic structures of copper oxide clusters during the methanation reaction. This breakthrough helps understand how cluster size and structure influence catalytic activity.

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Area of Science:

  • Materials Science
  • Catalysis
  • Computational Chemistry

Background:

  • Supported subnanometer metal oxide clusters exhibit enhanced reactivity, but their atomic-level structural characterization remains challenging.
  • X-ray absorption near edge structure (XANES) spectroscopy is sensitive to local geometry, and theoretical modeling of XANES spectra is reliable.
  • Supervised machine learning offers a powerful approach to extract structural information from experimental spectra.

Purpose of the Study:

  • To apply a supervised machine learning method to analyze grazing incidence XANES spectra of size-selective copper oxide clusters.
  • To correlate experimental XANES data with atomic-level structural descriptors under operando methanation conditions.

Main Methods:

  • Utilized a supervised machine learning approach, specifically a convolution neural network, trained on theoretical XANES spectra.
  • Applied the trained model to "invert" experimental grazing incidence XANES data from copper oxide clusters.
  • Measured spectra in operando conditions during the methanation reaction.

Main Results:

  • Successfully extracted Cu-Cu coordination numbers, serving as structural descriptors.
  • Distinguished between different structural motifs, specifically Cu2O-like and CuO-like, of copper oxide clusters.
  • Reliably evaluated average cluster sizes, demonstrating dynamic structural transformations during the reaction.

Conclusions:

  • The machine learning approach effectively bridges experimental XANES data and atomic-level structural information for supported subnanometer clusters.
  • Understanding the dynamic structural changes of copper oxide clusters under reaction conditions is crucial for catalysis.
  • This work has significant implications for establishing structure-composition-function relationships in heterogeneous catalysis.