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

ABSTRACT Recent advances in artificial intelligence (AI) have propelled materials discovery by identifying unique composition pathways at unprecedented speed. However, experimental characterization—the step where new materials are actually tested—still lags behind. Traditional characterization requires specialized instruments that measure electromagnetic responses in a painstaking, expert-driven process. SpectroGen offers a transformative solution. By coupling physics-inspired distribution models (e.g., Gaussians and Lorentzians) with a robust variable autoencoder framework, SpectroGen rapidly generates “virtual” spectra that correlate almost perfectly with actual measurements. This approach effectively bridges the gap between AI-driven materials discovery and real-world verification. SpectroGen’s universal compatibility also makes it flexible: any spectroscopy technique that can be represented by analytic functions may be harnessed within its platform. The potential impact is substantial. High-throughput screening—vital for developing next-generation catalysts, batteries, superconductors, and pharmaceuticals—can now be accelerated without sacrificing accuracy. Researchers stand to gain significant time and resource savings, as they can prioritize the most promising candidate materials for detailed follow-up. This synergy of fast AI-driven discovery and swift AI-enabled characterization could catalyze breakthroughs vital to society, from clean energy solutions to advanced medical treatments. Beyond accelerating fundamental research, SpectroGen’s capacity for rapid prototyping and validation is poised to reshape how we innovate, ultimately translating into critically needed technologies that better serve humanity.

Contributors: Yanmin Zhu, Loza F. Tadesse