Lawrence Berkeley National Laboratory

Autoencoder-transformer model applicable to both simulated and experimental XAS spectra is developed to predict the oxidation state of copper. X-ray absorption spectroscopy (XAS) and electron energy-loss spectroscopy (EELS) produce detailed information about oxidation state, bonding, and coordination, making them essential for quantitative studies of redox and structure in functional materials. However, high-throughput quantitative analysis of these spectra, especially for mixed valence materials, remains challenging as diverse experimental conditions introduce noise, misalignment, and broadening of the spectral features. We address this challenge by training a machine learning model consisting of an autoencoder to standardize the spectra and a transformer model to predict both Cu oxidation state and Bader charge directly from L-edge spectra. The model is trained on a large dataset of FEFF-simulated spectra, and its performance is evaluated on both simulated and experimental data. The results of the machine learning model exhibit accurate and transferable predictions across the domains of simulated and experimental spectra. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions.