A new AI tool developed by MIT engineers could revolutionize quality control in materials manufacturing by dramatically speeding up and reducing the cost of spectral analysis. Called SpectroGen, the generative AI system acts as a virtual spectrometer, capable of predicting what a material’s spectrum would look like in one type of scanning method—such as X-ray—based on data from another, like infrared, with 99 percent accuracy. Traditionally, verifying the quality of materials requires multiple expensive and time-consuming scans using different instruments. Each modality reveals distinct properties: infrared identifies molecular groups, X-ray diffraction shows crystal structures, and Raman scattering captures molecular vibrations. These measurements often require separate, bulky equipment and can take hours or days to complete. SpectroGen bypasses this bottleneck by using mathematical patterns inherent in spectral data. Instead of modeling complex chemical bonds, the tool treats spectra as mathematical curves—such as Gaussian or Lorentzian distributions—making it easier for AI to learn and predict relationships between different types of scans. This physics-informed approach allows the model to generate accurate spectra in under a minute, a thousand times faster than conventional methods. The system was trained on a dataset of over 6,000 mineral samples, each with known elemental compositions and multi-modal spectral data. After training, SpectroGen accurately predicted spectra in one modality from another, even for minerals not included in the training set. The results matched real-world measurements with remarkable precision. In manufacturing, this means a single, low-cost infrared scanner could be used on a production line, with SpectroGen generating the equivalent of X-ray or Raman data for quality assessment. This could streamline processes in industries developing advanced batteries, semiconductors, and pharmaceuticals. The tool’s potential extends beyond materials science. Researchers are exploring its use in disease diagnostics and agricultural monitoring, supported by a Google-funded project. The team, led by MIT’s Loza Tadesse and former postdoc Yanmin Zhu, is also launching a startup to bring SpectroGen to market. Tadesse envisions the AI as a co-pilot for scientists and technicians, enhancing efficiency and enabling faster innovation across sectors—from healthcare to defense. By turning spectral analysis into a rapid, accessible process, SpectroGen could accelerate the development and deployment of next-generation technologies.