Predictive autoencoder-transformer model of Cu oxidation state from EELS and XAS spectra

TL;DR

This paper introduces a machine learning model combining an autoencoder and transformer to accurately determine copper oxidation states from XAS and EELS spectra, handling noise and misalignment for high-throughput analysis.

Contribution

A novel autoencoder-transformer model trained on simulated data for precise, high-throughput oxidation state prediction from spectra, improving analysis of redox processes.

Findings

High accuracy in predicting Cu oxidation states on simulated data

Effective performance on experimental XAS and EELS spectra

Enables quantitative analysis of Cu redox in various conditions

Abstract

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, 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 evaluates model performance on both simulated and experimental data. The results of the machine learning model exhibit highly accurate prediction across the domains of simulated and experimental XAS as well as experimental EELS. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions.