Discovered Materials, a months-old San Francisco startup using AI models to find new substances for building advanced chips and part of Y Combinator’s spring 2026 batch, announced a million-dollar challenge for AI models to solve five critical problems in materials science. The company recruited an advisory group of academics at leading global universities to spell out five open problems and is offering a pool of prizes worth $1 million in total for solutions: a theory to explain cuprate superconductivity, or the ability of certain materials to have zero electrical resistance at ultra-low temperatures; a numerical method to predict the structures of interfaces between silicon and silicon dioxide, a material combination found in almost all semiconductors; a numerical method to map grain structure to thermal properties, enabling the design of better heat dissipation materials; a theory to predict why shining light on materials can change their behavior, particularly around superconductivity; and a numerical method to improve density functional theory, a key technique used in materials science to model the structures of atoms and molecules. Before announcing the challenges, Discovered Materials turned frontier lab models loose on them, giving AI agents internet access and other resources to work autonomously for days; the models spent several days on each problem without making much headway. Stanford professor Felipe Jornada, who helped develop the challenges, said explaining observed phenomena would improve understanding of physics and directly shape how electronics are built, energy is stored, and future materials are manufactured. Co-founder Advaith Sridhar said it is not particularly a