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Lookup NU author(s): Dr Thomas PopeORCiD, Dr Bowen LiORCiD, Professor Thomas PenfoldORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
X-ray spectroscopy provides sensitive, element-specific insight into local geometric and electronic structures, but predictive first-principles simulations can be computationally expensive for large and chemically diverse molecular systems. Recent machine-learning approaches have shown promise in accelerating structure-to-spectrum prediction; however, most directly regress discretized spectral intensities and rely on hand-crafted geometric descriptors centered on the absorbing atom. Herein, we introduce a machine learning framework that encodes a detailed, environment-aware representation of the nuclear structure beyond the absorbing site. The model combines these descriptors with a physically motivated, multiscale Gaussian spectral basis whose coefficients are obtained via ridge projection, enforcing smoothness and spectral consistency. To further enhance robustness across chemical and conformational diversity, we employ a multiscale structural similarity loss that couples geometric and spectral resolution. This integrated approach yields accurate and transferable predictions across a wide range of molecular geometries and chemical environments while maintaining physical interpretability. The proposed framework establishes a physically structured and scalable route to machine-learned X-ray spectroscopy.
Author(s): Pope TJ, Li B, Junkawitsch H, Bande A, Penfold TJ
Publication type: Article
Publication status: Published
Journal: Journal of Physical Chemistry A
Year: 2026
Pages: epub ahead of print
Online publication date: 06/05/2026
Acceptance date: 15/04/2026
Date deposited: 12/05/2026
ISSN (print): 1089-5639
ISSN (electronic): 1520-5215
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acs.jpca.6c01127
DOI: 10.1021/acs.jpca.6c01127
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