| Autor: |
Kotobi A; Helmholtz-Zentrum Hereon, Institute of Surface Science, Geesthacht, DE 21502, Germany., Singh K; Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Berlin, DE 10409, Germany.; Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, DE 14195, Germany., Höche D; Helmholtz-Zentrum Hereon, Institute of Surface Science, Geesthacht, DE 21502, Germany., Bari S; Deutsches Elektronen-Synchrotron DESY, Hamburg, DE 22607, Germany.; Zernike Institute for Advanced Materials, University of Groningen, Groningen 9712, Netherlands., Meißner RH; Helmholtz-Zentrum Hereon, Institute of Surface Science, Geesthacht, DE 21502, Germany.; Hamburg University of Technology, Institute of Polymers and Composites, Hamburg, DE 21073, Germany., Bande A; Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Berlin, DE 10409, Germany.; Leibniz University Hannover, Institute of Inorganic Chemistry, Hannover, DE 30167, Germany. |
| Abstrakt: |
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. |
