SpectroGen—A Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning

Spectroscopy is a key analytical tool in the physical and biological sciences, but its reliance on specialized instrumentation for different physical phenomena limits its accessibility and widespread use. In this study, we introduce SpectroGen, a novel deep generative model that leverages physical priors to generate accurate spectral signatures across different modalities, using input from only a single modality. By redefining spectral data as mathematical distributions rather than traditional representations based on physical or molecular states, SpectroGen enables broader application. Tested on 319 standard mineral samples, the model demonstrated a 99% correlation with ground truth data and a root mean square error of 0.01, achieving superior resolution compared to experimentally collected spectra. The model successfully transferred spectral information across Raman, Infrared, and X-ray Diffraction modalities using Gaussian, Lorentzian, and Voigt distribution priors, respectively, but its framework is adaptable to any spectral input represented by a distribution prior. This makes SpectroGen a universally applicable tool, overcoming the limitations of traditional spectroscopy methods and potentially revolutionizing fields like materials science, pharmaceuticals, and biology by reducing the need for expensive, specialized equipment while providing high-quality spectral data.