ArticleJournal of the American Chemical Society2023
Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra.
Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From Local Atomic Structure to X-ray Spectra: Absorber-Centric Machine-Learning Encoding.The journal of physical chemistry. A · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Interpretable data-driven discovery of high-performance multielement metal oxide electrocatalysts for oxygen evolution reaction from a small hybrid dataset.Science and technology of advanced materials · 2026Article
- VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.Journal of cheminformatics · 2025Article
- Graph Neural Networks vs. Traditional QSAR: A Comprehensive Comparison for Multi-Label Molecular Odor Prediction.Molecules (Basel, Switzerland) · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.ACS omega · 2025Article
- A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML.Journal of synchrotron radiation · 2025Article
- Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Recent progress of black phosphorene from preparation to diversified bio-/chemo-nanosensors and their challenges and opportunities for comprehensive health.Mikrochimica acta · 2024Review
- Article
- Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.Journal of the American Chemical Society · 2024Article
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Authors and funding
6 authors.
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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.
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