A YC startup is putting $1M behind five unsolved materials science problems for AI

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.

• 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.
• A numerical method to improve Density Functional Theory, a key technique used in materials science to model the structures of atoms and molecules.