YC Startup Discovered Materials Puts $1M Behind Five Unsolved Materials Science Problems to Test AI
San Francisco-based startup Discovered Materials has announced a $1 million prize pool for solving five key open problems in materials science, aiming to create a concrete benchmark for AI progress in complex physics.
Discovered Materials, a San Francisco-based startup that uses AI models to discover new substances for advanced chips, announced on November 5 a $1 million prize challenge for AI models capable of solving five critical unsolved problems in materials science. The company, part of Y Combinator’s spring 2026 batch, aims to address the gap between AI's success in mathematics and its limited progress in complex physical systems.
While frontier AI models have solved outstanding math problems and made strides in life sciences, they have yet to truly plumb the complex physics underlying 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, co-founder of Discovered Materials. To define these challenges, the company recruited an advisory group of academics from leading global universities, including Stanford professor Felipe Jornada.
The five problems include developing a theory to explain cuprate superconductivity, creating numerical methods to predict silicon-silicon dioxide interface structures, mapping grain structure to thermal properties, predicting how light changes material behavior, and improving Density Functional Theory (DFT). Sridhar stated that the goal is to establish these problems as “goalposts” for the field, regardless of who solves them—whether it be AI, professors, or other researchers.
Before announcing the challenge, Discovered Materials tested frontier lab models against these problems, giving AI agents internet access and resources to work autonomously for days. The models spent several days on each problem without making much headway. However, Sridhar noted that tracking efforts to solve these problems will show “whether the models are climbing in this domain,” providing a clearer metric for AI's evolving capabilities in scientific discovery.