Fingerprint Analysis of X-ray Absorption Spectra with the Machine-Learning Method Trained on the Multielement Experimental Library

Abstract

X-ray absorption near edge structure (XANES) spectroscopy is a powerful method to probe the oxidation state and local structure of metals in catalytic materials. However, it suffers from a lack of unbiased data analysis protocols. Machine learning (ML) overcomes human-related factors by uncovering relevant spectrum-structure relationships and resulting cross-validation analysis. The bottlenecks in the automatic processing of experimental data are the lack of chemically diverse XANES reference libraries and the systematic differences between theory and experiment. Therefore, compiling experimental reference libraries across the periodic table and rational application of ML methodology to small (in terms of data science) training data sets becomes increasingly important. This work revises the classical XANES fingerprint analysis by database augmentation, feature extraction, cross-validation, and uncertainty analysis. We apply the developed methodology to decipher the oxidation state and local coordination of supported vanadium-oxo species (VO_x), which change their structure by participating in oxidative dehydrogenation catalysis. The developed Cr K-edge and V K-edge experimental library and instruments for analysis may serve as a starting point for a unified platform for fingerprint XANES data analysis.