Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning

TL;DR

SpectroGen is a universal deep generative model that synthesizes accurate spectral signatures across different modalities using a single spectral input, leveraging distribution-based representations and physical priors.

Contribution

Introduces SpectroGen, a novel model that enables spectral transfer across modalities using distribution representations and physical priors, broadening spectroscopy applications.

Findings

Achieved 99% correlation with ground truth spectra

Demonstrated transfer across Raman, Infrared, and X-ray modalities

Superior resolution compared to experimental spectra

Abstract

Spectroscopy is a powerful analytical technique for characterizing matter across physical and biological realms1-5. However, its fundamental principle necessitates specialized instrumentation per physical phenomena probed, limiting broad adoption and use in all relevant research. In this study, we introduce SpectroGen, a novel physical prior-informed deep generative model for generating relevant spectral signatures across modalities using experimentally collected spectral input only from a single modality. We achieve this by reimagining the representation of spectral data as mathematical constructs of distributions instead of their traditional physical and molecular state representations. The results from 319 standard mineral samples tested demonstrate generating with 99% correlation and 0.01 root mean square error with superior resolution than experimentally acquired ground truth spectra. We showed transferring capability across Raman, Infrared, and X-ray Diffraction modalities with Gaussian, Lorentzian, and Voigt distribution priors respectively6-10. This approach however is globally generalizable for any spectral input that can be represented by a distribution prior, making it universally applicable. We believe our work revolutionizes the application sphere of spectroscopy, which has traditionally been limited by access to the required sophisticated and often expensive equipment towards accelerating material, pharmaceutical, and biological discoveries.