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 Hernández-León; 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^*
, Markus Hirvensalo
, Patricia Hernández-León
, Anja Aarva
, Jarkko Etula
, Toma Susi
, Patrick Rinke
, Tomi Laurila
, Miguel A. Caro^*

^*Tämän työn vastaava kirjoittaja

Technische Universität Dresden
University of Vienna

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

55 Sitaatiot (Scopus)

214 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivut	6240−6254
Julkaisu	Chemistry of Materials
Vuosikerta	34
Numero	14
DOI - pysyväislinkit	https://doi.org/10.1021/acs.chemmater.1c04279
Tila	Julkaistu - 13 heinäk. 2022
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

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

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10.1021/acs.chemmater.1c04279Lisenssi: CC BY

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based MaterialsFinal published version, 5,59 MBLisenssi: CC BY

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NEXTCELL: Next generation interatomic potentials to simulate new cellulose based materials
Caro, M. (Vastuullinen johtaja)
01/09/2020 → 31/08/2025
Projekti: RCF Academy Research Fellow (new)
LEARNSOLAR: Rinke-LearnSolar
Rinke, P. (Vastuullinen johtaja), Henkel, P. (Projektin jäsen), Hoffmann, G. (Projektin jäsen), Fangnon, A. (Projektin jäsen), Dvorak, M. (Projektin jäsen), Mikkola, T. (Projektin jäsen), Laakso, J. (Projektin jäsen) & Homm, H. (Projektin jäsen)
01/09/2020 → 31/08/2024
Projekti: RCF Academy Project
-: Hiilimonoksidin, metaanin ja metanolin tuotanto hiilidioksidin sähkökemiallisella pelkistyksellä
Caro, M. (Vastuullinen johtaja), Pršlja, P. (Projektin jäsen), Ibragimova, R. (Projektin jäsen), Kuklin, M. (Projektin jäsen), Lehtomäki, J. (Projektin jäsen), Heinolainen, A. (Projektin jäsen), Hernandez Leon, P. (Projektin jäsen), Kloppenburg, J. (Projektin jäsen), Järvinen, K. (Projektin jäsen) & Zarrouk, T. (Projektin jäsen)
01/01/2020 → 31/12/2023
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Aalto University: Machine Learning Gives Material Science Researchers a Peek at the Answer Key
Caro, M. A.
22/07/2022
1 kohde/ Medianäkyvyys
Lehdistö/media: Esiintyminen mediassa
A model trained to predict spectroscopic profiles helps to decipher the structure of materials
Caro, M. A. & Golze, D.
21/07/2022
1 kohde/ Medianäkyvyys
Lehdistö/media: Esiintyminen mediassa

Siteeraa tätä

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, 34(14), 6240−6254. https://doi.org/10.1021/acs.chemmater.1c04279

@article{539299cff5e346a4853de7a0c2c83472,

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

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. ",

author = "Dorothea Golze and Markus Hirvensalo and Patricia Hern{\'a}ndez-Le{\'o}n and Anja Aarva and Jarkko Etula and Toma Susi and Patrick Rinke and Tomi Laurila and Caro, \{Miguel A.\}",

note = "| 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{\textquoteright}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: {\textcopyright} ",

year = "2022",

month = jul,

day = "13",

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

language = "English",

volume = "34",

pages = "6240−6254",

journal = "Chemistry of Materials",

issn = "0897-4756",

publisher = "American Chemical Society",

number = "14",

}

Golze, D, Hirvensalo, M, Hernández-León, 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, Vuosikerta 34, Nro 14, Sivut 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; Hirvensalo, Markus; Hernández-León, Patricia et al.
julkaisussa: Chemistry of Materials, Vuosikerta 34, Nro 14, 13.07.2022, s. 6240−6254.

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

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 - Hernández-León, Patricia

AU - Aarva, Anja

AU - Etula, Jarkko

AU - Susi, Toma

AU - Rinke, Patrick

AU - Laurila, Tomi

AU - Caro, Miguel A.

N1 - | 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: ©

PY - 2022/7/13

Y1 - 2022/7/13

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85135248837

U2 - 10.1021/acs.chemmater.1c04279

DO - 10.1021/acs.chemmater.1c04279

M3 - Article

AN - SCOPUS:85135248837

SN - 0897-4756

VL - 34

SP - 6240−6254

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

Abstrakti

Rahoitus

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

NEXTCELL: Next generation interatomic potentials to simulate new cellulose based materials

LEARNSOLAR: Rinke-LearnSolar

-: Hiilimonoksidin, metaanin ja metanolin tuotanto hiilidioksidin sähkökemiallisella pelkistyksellä

Laitteet

Science-IT

Lehtileikkeet

Aalto University: Machine Learning Gives Material Science Researchers a Peek at the Answer Key

A model trained to predict spectroscopic profiles helps to decipher the structure of materials

Siteeraa tätä