Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and \(\boldsymbol{GW}\)

We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with \(GW\) and uses kernel ridge...

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Bibliographic Details
Published in:arXiv.org 2021-12
Main Authors: Golze, Dorothea, Hirvensalo, Markus, Hernández-León, Patricia, Aarva, Anja, Etula, Jarkko, Toma Susi, Rinke, Patrick, Laurila, Tomi, Caro, Miguel A
Format: Article
Language:English
Subjects:
Binding energy
Carbon
Density functional theory
Machine learning
Microprocessors
Photoelectrons
Qualitative analysis
Spectra
Spectrum analysis
Structural models
X ray photoelectron spectroscopy
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Summary:We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with \(GW\) and uses kernel ridge regression for the ML predictions. We apply the new approach to materials and molecules containing carbon, hydrogen and oxygen, and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.
ISSN:2331-8422