Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Author
Date
2022-07-13
Department
Department of Applied Physics
Department of Electrical Engineering and Automation
Department of Chemistry and Materials Science
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Mcode
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Language
en
Pages
6240−6254
Series
Chemistry of Materials, Volume 34, issue 14
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.
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| openaire: EC/H2020/756277/EU//ATMEN Funding Information: The authors acknowledge funding from the Academy of Finland under Projects 316168 (D.G.), 334532 (P.R.), 310574, 329483, 330488 (M.A.C.), and 321713 (M.A.C. and P.H.-L.) and from their flagship program Finnish Center for Artificial Intelligence (FCAI), from the Emmy Noether Programme of the German Research Foundation under Project Number 453275048 (D.G.), from COST action CA18234, and from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 756277-ATMEN (T.S.). Computing time from CSC–IT Center for Science, allocated for the Grand Challenge Project XPEC, is gratefully acknowledged. Part of this work was carried out during a HPC-Europa3 mobility exchange (Horizon 2020 Program under Grant Agreement 730897). We thank V.L. Deringer for providing the a-CO structural models used in this study. x Publisher Copyright: ©
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DOI
10.1021/acs.chemmater.1c04279
Citation
Golze, D, Hirvensalo, M, Hernández-León, P, Aarva, A, Etula, J, Susi, T, Rinke, P, Laurila, 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, vol. 34, no. 14, pp. 6240−6254 . https://doi.org/10.1021/acs.chemmater.1c04279
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https://urn.fi/URN:NBN:fi:aalto-202208244989
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[article-cris] Kemian tekniikan korkeakoulu / CHEM