aceAI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure
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…
A graph neural network can predict full EXAFS spectra directly from atomic coordinates, removing manual parameter selection and allowing inference across many candidate structures.
Evaluation used FEFF-generated spectra for transition-metal complexes, and the authors frame extension to experimentally relevant environments as future work rather than a demonstrated result.
Evidence
- Peer-reviewedAdvanced Science2026-10-18
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Truvace Impact Record TRV-2026-1975, v1: “AI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure.” Truvace, 2026-10-20. /record/TRV-2026-1975 (accessed at citation time). sha256 7db5baad2a3b65d5…
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