Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

TL;DR

This paper presents a supervised machine learning approach using a random forest model to accurately predict copper oxidation states from EELS and XAS spectra, enabling faster and more quantitative analysis of materials.

Contribution

The study introduces a novel machine learning model that predicts copper oxidation states directly from spectral data, improving analysis speed and accuracy over traditional matching methods.

Findings

Achieved an R^2 score of 0.85 on simulated data.

Successfully predicted experimental spectra and mixed valence samples.

Potential for real-time spectral analysis in materials research.

Abstract

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.85 and a root mean square valence error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.