X-ray photoelectron spectroscopy (XPS) is a surface sensitive (<10 nm) characterisation technique used to investigate material properties including chemical composition, chemical depth distribution and electronic structure [1, 2]. Especially in recent years, XPS has become a reliable and advanced experimental technique across various disciplines of science and engineering resulting in the generation of large spectral datasets. Extracting quantitative information from these datasets has traditionally required trained spectroscopists to perform empirical peak-fitting routines for each individual spectrum. For example, to obtain the atomic fraction of a particular element in a sample, the expert needs to determine the integrated peak area from non-trivial fitting routines based on the inelastic scattering background and zero energy loss line shapes and normalize it according to relative sensitivity factors [3]. In response to the increasing demand for reliable and instantaneous spectral analysis, we propose an automated quantitative X-ray photoelectron spectrum analysis pipeline by combining the Simulation of Electron Spectra for Surface Analysis (SESSA) software with a convolutional neural network (CNN). SESSA serves as an important tool in the field of XPS either as a database for material parameter retrieval or as a Monte Carlo-based simulation software for quantitative interpretation of XP spectra or Auger electron spectra (AES) for a variety of materials (bulk, nanostructures, layered spheres, etc.) [4, 5]. In this work SESSA is applied to generate approximately 250’000 spectra for 2500 materials (of varying complexity) and single elements, illustrated by the histogram overlaid with the periodic table in Fig. 1. To increase the variability in the simulated dataset and to reflect experimental conditions we simulated spectra with different chemical shifts, different peak widths and different peak shapes (Gauss, Lorentz, Doniach-Sunjic). In a first preliminary study, these simulated spectra together with their corresponding chemical labels were used to train a CNN to classify the chemical abundance. The aim of the study is to investigate the feasibility of applying deep learning models to high-throughput material characterization within XPS. In the future, we plan to compare their performance on experimental data with classical peak-fitting routines and to further improve the deep learning model to predict more complex sample features such as thin film thickness and electronic properties. [1] D. Nanda Gopala Krishna and John Philip, Applied Surface Science Advances 12 (2022), p. 100332. [2] Grzegorz Greczynski et al., Nature Reviews Methods Primers 3.1 (May 2023), p. 40. [3] Alexander G. Shard, Journal of Vacuum Science & Technology A 38.4 (July 2020), p. 041201. [4] C. J. Powell, W. Smekal, and W. S. M. Werner, AIP Conference Proceedings 788.1 (Sept. 2005), pp. 107–111. [5] Wolfgang S. M. Werner and Cedric J. Powell. Journal of Vacuum Science & Technology A 39.6 (Sept. 2021), p. 063205.