Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

  • Amir Kotobi
  • Kanishka Singh
  • Daniel Höche
  • Sadia Bari
  • Robert H. Meißner
  • Annika Bande

Organisationseinheiten

Externe Organisationen

  • Helmholtz-Zentrum Geesthacht Zentrum für Material- und Küstenforschung GmbH
  • Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
  • Freie Universität Berlin (FU Berlin)
  • Deutsches Elektronen-Synchrotron (DESY)
  • Reichsuniversität Groningen
  • Technische Universität Hamburg (TUHH)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)22584-22598
Seitenumfang15
FachzeitschriftJournal of the American Chemical Society
Jahrgang145
Ausgabenummer41
Frühes Online-Datum9 Okt. 2023
PublikationsstatusVeröffentlicht - 18 Okt. 2023

Abstract

The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models' predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum.

ASJC Scopus Sachgebiete

Zitieren

Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. / Kotobi, Amir; Singh, Kanishka; Höche, Daniel et al.
in: Journal of the American Chemical Society, Jahrgang 145, Nr. 41, 18.10.2023, S. 22584-22598.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Kotobi A, Singh K, Höche D, Bari S, Meißner RH, Bande A. Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. Journal of the American Chemical Society. 2023 Okt 18;145(41):22584-22598. Epub 2023 Okt 9. doi: 10.1021/jacs.3c07513
Kotobi, Amir ; Singh, Kanishka ; Höche, Daniel et al. / Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. in: Journal of the American Chemical Society. 2023 ; Jahrgang 145, Nr. 41. S. 22584-22598.
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