Neural network-assisted analysis of X-ray absorption spectra of metal oxide clusters

It is challenging to understand the reactivity from structure perspective for the supported metal oxide clusters. Many operando characterization techniques for solving such challenge are limited due to the low-metal loading and high temperature condition. Because of the sensitivity of X-ray absorption near edge (XANES) to the local structure, we demonstrated that XANES can be analyzed and provide structural information combing with supervised machine learning method. In this work, we apply the neural network method to the analysis of grazing incidence XANES spectra of size-selective Cu oxide clusters on flat support, measured in operando condition. The convolution neural network was trained to build the correlation between the XANES and structural descriptors (Cu-Cu coordination numbers). Our result indicates that we can distinguish between different structural motifs of Cu oxide cluster during the reaction conditions and invert the experimental XANES to obtain structure parameters which helps the understanding of the structure-properties relation of the catalysts.