Discovered Materials, a months-old, San Francisco startup using AI models to find new substances for building advanced chips, announced a million-dollar challenge for AI models to solve five critical problems in materials science.

While frontier AI models have solved a remarkable series of outstanding mathematics problems and made progress in the life sciences, they have yet to truly plumb the complex physics in advanced materials.

“It’s more difficult to specify problems clearly in materials science because it’s not like math, where you have a very specific definition and can get to a yes or no,” said Advaith Sridhar, a co-founder of Discovered Materials, which was part of Y Combinator’s spring 2026 batch. “We came to this discussion [wondering], what are the grand challenges in this field? How do we know AI is making progress?”

Though materials science doesn’t have an equivalent of the Millennium Prize Problems, Discovered Materials recruited an advisory group of academics at leading global universities to spell out five open problems. The company is offering a pool of prizes, worth $1 million in total, for solutions to the problems.

“Across materials science, there are phenomena we observe but cannot explain,” Felipe Jornada, a Stanford professor who helped develop the challenges, said in a statement. “Explaining these phenomena would improve our understanding of physics, and the answers would directly shape how we build electronics, store energy, and manufacture the materials of the future.”

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.

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