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

Dorothea Golze; Markus Hirvensalo; Patricia Hernandez-Leon; Anja Aarva; Jarkko Etula; Toma Susi; Patrick Rinke; Tomi Laurila; Miguel A. Caro

doi:10.1021/acs.chemmater.1c04279

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

Dorothea Golze (Corresponding author), Markus Hirvensalo, Patricia Hernandez-Leon, Anja Aarva, Jarkko Etula, Toma Susi, Patrick Rinke, Tomi Laurila, Miguel A. Caro (Corresponding author)

Physics of Nanostructured Materials

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Pages (from-to)	6240-6254
Number of pages	15
Journal	Chemistry of Materials
Volume	34
Issue number	14
DOIs	https://doi.org/10.1021/acs.chemmater.1c04279
Publication status	Published - 26 Jul 2022

Austrian Fields of Science 2012

104026 Spectroscopy
103018 Materials physics

Keywords

X-RAY SPECTROSCOPY
MOLECULES
APPROXIMATION
SURFACES
SPECTRA

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10.1021/acs.chemmater.1c04279Licence: CC BY 4.0

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Golze, D., Hirvensalo, M., Hernandez-Leon, 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, 34(14), 6240-6254. https://doi.org/10.1021/acs.chemmater.1c04279

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

keywords = "X-RAY SPECTROSCOPY, MOLECULES, APPROXIMATION, SURFACES, SPECTRA",

author = "Dorothea Golze and Markus Hirvensalo and Patricia Hernandez-Leon and Anja Aarva and Jarkko Etula and Toma Susi and Patrick Rinke and Tomi Laurila and Caro, {Miguel A.}",

year = "2022",

month = jul,

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doi = "10.1021/acs.chemmater.1c04279",

language = "English",

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Golze, D, Hirvensalo, M, Hernandez-Leon, P, Aarva, A, Etula, J, Susi, T, Rinke, P, Laurila, T & Caro, MA 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

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW. / Golze, Dorothea (Corresponding author); Hirvensalo, Markus; Hernandez-Leon, Patricia et al.
In: Chemistry of Materials, Vol. 34, No. 14, 26.07.2022, p. 6240-6254.

Publications: Contribution to journal › Article › Peer Reviewed

TY - JOUR

T1 - Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

AU - Golze, Dorothea

AU - Hirvensalo, Markus

AU - Hernandez-Leon, Patricia

AU - Aarva, Anja

AU - Etula, Jarkko

AU - Susi, Toma

AU - Rinke, Patrick

AU - Laurila, Tomi

AU - Caro, Miguel A.

PY - 2022/7/26

Y1 - 2022/7/26

KW - X-RAY SPECTROSCOPY

KW - MOLECULES

KW - APPROXIMATION

KW - SURFACES

KW - SPECTRA

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U2 - 10.1021/acs.chemmater.1c04279

DO - 10.1021/acs.chemmater.1c04279

M3 - Article

SN - 0897-4756

VL - 34

SP - 6240

EP - 6254

JO - Chemistry of Materials

JF - Chemistry of Materials

IS - 14

ER -

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

Austrian Fields of Science 2012

Keywords

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ATMEN: Atomic precision materials engineering

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

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

Projects

ATMEN: Atomic precision materials engineering

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