SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization

Abstract

Artificial intelligence (AI)-driven materials discovery offers rapid design of novel material compositions, yet synthesis and characterization lag behind. Characterization, in particular, remains bottlenecked by labor-intensive experiments using expert-operated instruments that typically rely on electromagnetic spectroscopy. We introduce SpectroGen, a generative AI model for transmodality spectral generation, designed to accelerate materials characterization. SpectroGen generates high-resolution, high-signal-to-noise ratio spectra with 99% correlation to ground truth and a root-mean-square error of 0.01 a.u. Its performance is driven by two key innovations: (1) a novel distribution-based physical prior and (2) a variational autoencoder (VAE) architecture. The prior simplifies complex structural inputs into interpretable Gaussian or Lorentzian distributions, while the VAE maps them into a physically grounded latent space for accurate spectral transformation. SpectroGen generalizes across spectral domains and promises rapid, accurate spectral predictions, potentially transforming high-throughput discovery in domains such as battery materials, catalysts, superconductors, and pharmaceuticals.