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Integrating explainability into graph neural network models for the prediction of X-ray absorption spectra
Publikationstyp
Journal Article
Date Issued
2023-10-09
Sprache
English
Author(s)
Singh, Kanishka
Helmholtz-Zentrum hereon GmbH, Institut für Werkstoffforschung
Bande, Annika
Volume
145
Issue
41
Start Page
22584
End Page
22598
Citation
Journal of the American Chemical Society 145 (41): 22584-22598 (2023-10-09)
Publisher DOI
Scopus ID
Publisher
American Chemical Society
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.
Subjects
MLE@TUHH
DDC Class
500: Science