A San Francisco-based startup called Discovered Materials, founded just months ago, uses AI models to discover new materials for advanced chip manufacturing. The company has announced a million-dollar challenge inviting AI models to tackle five critical problems in materials science. While frontier AI systems have already solved a notable series of outstanding mathematics problems and made strides in the life sciences, they have yet to truly penetrate the complex physics of 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," explained Advaith Sridhar, a co-founder of Discovered Materials, which participated in 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."

Although materials science lacks an equivalent to the Millennium Prize Problems, Discovered Materials assembled an advisory group of academics from leading global universities to articulate five open problems. The company is offering a prize pool totaling $1 million for solutions to these challenges:

- A theory to explain cuprate superconductivity—the ability of certain materials to exhibit 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 nearly 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 alter their behavior, particularly regarding 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," said Felipe Jornada, a Stanford professor who helped develop the challenges, 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 publicly announcing the challenges, Discovered Materials gave frontier lab models access to the problems, providing AI agents with internet access and other resources to work autonomously for days. The models spent several days on each problem without making significant progress. Nevertheless, it's easy to imagine OpenAI or Anthropic attempting to solve them in one shot with substantially more compute or a newer model. "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 to solve the problems will reveal "whether the models are climbing in this domain," he added. The challenge also reflects the evolving nature of scientific inquiry as AI demonstrates the ability to uncover insights and techniques that human researchers didn't anticipate. With solutions apparently easier to find than ever, the questions themselves become more important. "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."

Discovered Materials' primary focus is developing new materials for use in semiconductors; the startup partners with chip giants on research projects while also funding its own scientific efforts. "Those timelines have accelerated a little bit," Sridhar 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."