FAIRmat Users Meeting | Quantitative XPS Analysis Using CNNs - Lukas Pielsticker

Описание: Quantitative XPS Analysis Using Convolutional Neural Networks

X-ray photoelectron spectroscopy (XPS) is a powerful tool for studying the electronic structure and chemical composition of solid surfaces. Quantitative analysis of XP spectra typically relies on manual curve fitting by expert spectroscopists. However, recent advancements in the ease of use and reliability of XPS instruments have led to a growing number of (novice) users generating large datasets that are becoming difficult to analyze manually. Additionally, the expansion of publicly available XPS databases further increases the volume of data requiring efficient analysis. Reflecting these developments, more automated techniques are desirable to assist users in processing large XPS datasets.
Here we present a scalable framework for automated XPS quantification using convolutional neural networks (CNNs). By training CNN models on artificially generated XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), it is possible to obtain models for auto-quantification of transition metal XP spectra [1]. CNNs are shown to be capable of quantitatively determining the presence of metallic and oxide phases, achieving accuracy comparable to or exceeding that of conventional data analysis methods. The models are flexible enough to handle spectra containing multiple chemical elements and acquired under varying experimental conditions. The use of dropout variational inference for the determination of quantification uncertainty is discussed. Finally, we demonstrate how these network models are integrated into NOMAD [2], enabling real-time analysis of newly generated data.

References
[1] Pielsticker, L.; Nicholls, R.; DeBeer, S.; Greiner, M., Analytica Chimica Acta 2023 1271, 341433.
[2] Scheidgen, M., et al., Journal of Open Source Software 2023 8.90, 5388.

Lukas Pielsticker [1], [2], Rachel L. Nicholls [1], Serena DeBeer [1], Walid Hetaba [1], Florian Dobener [2], Laurenz Rettig [3], José A. Márquez [2], Sandor Brockhauser [2], Heiko Weber [4], Claudia Draxl [2], Mark Greiner [1]

[1] Max Planck Institute for Chemical Energy Conversion, Mülheim an der Ruhr, Germany
[2] Physics Department and CSMB, Humboldt-Universität zu Berlin, Germany
[3] Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society, Berlin, Germany
[4] Chair of Applied Physics, FAU Erlangen-Nürnberg, Erlangen, Germany