Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW
Golze D, Hirvensalo M, Hernandez-Leon P, Aarva A, Etula J, Susi T, Rinke P, Laurila T, Caro MA (2022)
Publication Type: Journal article
Publication year: 2022
Journal
Book Volume: 34
Pages Range: 6240-6254
Journal Issue: 14
DOI: 10.1021/acs.chemmater.1c04279
Abstract
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 disordered materials and small 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.
Involved external institutions
Finland (FI)
Austria (AT)How to cite
APA:
Golze, D., Hirvensalo, M., Hernandez-Leon, P., Aarva, A., Etula, J., Susi, T.,... Caro, M.A. (2022). Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW. Chemistry of Materials, 34(14), 6240-6254. https://doi.org/10.1021/acs.chemmater.1c04279
MLA:
Golze, Dorothea, et al. "Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW." Chemistry of Materials 34.14 (2022): 6240-6254.
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