Fingerprint Analysis of X-Ray Absorption Spectra of Catalysts: From Linear Combination Fit to Machine-Learning Trained on Multielement Experimental Library

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 the lack of unbiased data analysis protocols. Machine learning (ML) overcomes human-related factors by uncovering relevant spectrum-structure relationships and subsequent cross-validation analysis. The bottlenecks in the automatic processing of experimental data are the lack of chemically diverse XANES reference libraries and 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 datasets 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 (VOx), which change their structure participating in oxidative dehydrogenation catalysis. The developed library and instruments for analysis may serve as a starting point for a unified platform of fingerprint XANES data analysis.