X-Ray Photoelectron Spectroscopy Enhanced by Machine Learning

X-Ray photoelectron spectroscopy (XPS) is a technique for identifying individual elements in a mixture/compound. Samples are irradiated by X-rays and the kinetic energy of ejected electrons is measured. Ejected electrons are captured by a spectrometer and measured intensities are plotted versus kinetic energy (Fig. 1). Each element appears as a series of peaks distributed as Gaussians/Lorentzians. From these peaks, material properties can be estimated. Current software for XPS systems attempts, but often fails, to correctly classify the sample. We would like to enhance this classification using machine learning (ML) algorithms.