Authors
Javier Heras-Domingo, Andrea Ruiz-Ferrando, Adam H Clark, Stephan Pollitt, Sharon Mitchell, Javier Pérez-Ramírez, Núria López
Published in
Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e78226. Oct 18, 2026. Epub Oct 18, 2026.
Abstract
Understanding atomic structure across chemical systems relies heavily on extended X-ray absorption fine structure (EXAFS) because of its sensitivity to local atomic environments. However, quantitative interpretation remains inherently iterative, requiring structural parameters or models to be fitted against experimental spectra using explicit multiple-scattering calculations. Machine-learning approaches have accelerated EXAFS analysis, yet direct prediction of full spectra from atomistic structure across diverse chemical environments remains challenging. Here, we introduce AI-EXAFS, a physics-informed graph neural network that predicts spectra directly from three-dimensional atomic coordinates. Combining an absorber-centered graph representation with distance-modulated message passing and an EXAFS-specific training objective enables direct learning of structure-spectrum relationships without predefined structural descriptors or manual parameter selection. Trained and evaluated across a dataset of 85,991 transition-metal complexes with FEFF-generated spectra, AI-EXAFS achieves average held-out test-set R-factors of 0.043 in -space and 0.019 in -space, demonstrating accurate structure-to-spectrum prediction across a broad range of local chemical environments. AI-EXAFS establishes a scalable structure-to-spectrum framework for EXAFS, enabling direct inference across large sets of candidate structures and providing a foundation for data-driven prediction across increasingly complex and experimentally relevant environments.
PMID:
42893450
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.
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