  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.
