Deep neural network for X-ray photoelectron spectroscopy data analysis

Understand

In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental X-ray photoelectron spectroscopy data.

• Given the lack of a reliable database in literature, in order to train the neural network we computed a large ($>$100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon.
• The trained net performs as good as standard methods on a test set of $\approx$ 500 well characterized experimental X-ray photoelectron spectra.
• Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed.