YC Startup Discovered Materials Puts $1M Behind Five Unsolved AI Challenges

Discovered Materials, a months-old San Francisco startup from Y Combinator's spring 2026 batch, is offering $1 million in total prizes for AI models that solve five unsolved materials science problems.

The company uses AI models to find new substances for advanced chips. It recruited an advisory group of academics at leading global universities to define the five open problems, which it announced as a public challenge — a benchmark the field currently lacks.

"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. "We came to this discussion [wondering], what are the grand challenges in this field? How do we know AI is making progress?"

Materials science has no equivalent of the Millennium Prize Problems. These five challenges are an attempt to fill that gap.

What are the five problems?

The $1 million prize pool covers five specific challenges:

• A theory to explain cuprate superconductivity — the ability of certain materials to carry current with 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.

"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."

Five problems, $1 million — and every one of them sits inside the chips the industry ships today.

Can today's AI models solve them?

Not yet. Before announcing the challenges, Discovered Materials turned frontier lab models loose on the problems, giving AI agents internet access and other resources to work autonomously. The models spent several days on each problem without making much headway.

Still, the barrier may not hold. It's easy to imagine OpenAI or Anthropic attempting to one-shot the problems with significantly more compute or a newer model.

That outcome wouldn't bother the startup. "It's not particularly a concern to us whether we find it, or someone else finds it, or a professor finds it, or the frontier labs find it," Sridhar said. "The reason we're putting out these challenges is because we want them to serve as the goalposts. If you go and solve these challenges, we are deepening our understanding of materials and how things around us behave."

Tracking efforts on the problems will show "whether the models are climbing in this domain," he added.

The benchmark, not the prize money, is the point.

Why define questions first?

The challenge reflects a shift in how science gets done as AI uncovers insights human researchers didn't anticipate. When solutions become easier to find, the questions themselves become the scarce asset.

"This is a new way of thinking about science, where we come up with the verification and the challenges first, and then we let somebody come up with the ideas that explain the problem," Sridhar said. "Knowing what are the right questions to ask is not trivial and very difficult in this domain, so this is where the professors have really helped us shape what are the right questions we should be asking."

Verification first, ideas second — that inversion is the startup's real experiment.

What does this mean for the chip business?

Discovered Materials' main business is developing new materials for semiconductors. The startup partners with chip giants on research projects while also funding its own scientific efforts.

When the company announced its seed round in August, Sridhar told TechCrunch he hoped it would generate a useful material within a year. "Those timelines have accelerated a little bit," he says today. "I'm more confident now that we will be able to find these industrially useful materials in a timeline shorter than a year."

If frontier labs take the bait and the goalposts move, the whole field gets a scoreboard — and Discovered Materials gets a faster path to materials it can sell.