  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.
