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.
Pope et al. (Wed,) studied this question.