A YC startup is putting $1M behind 5 unsolved materials science problems for AI; plus, AI hardware startup Nominal triples its valuation

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0:50So you can focus on other important things, like savoring that last bit of pumpkin pie. From early startups to Fortune 500 companies, thousands are turning to Framer to build a website they'll love. Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com slash tcstartups for 30 % off a Framer Pro annual plan. That's framer.com slash tcstartups for 30 % off. Rules and restrictions may apply. Discovered Materials is a months-old San Francisco startup using AI models to find new substances for building advanced chips, and the company announced a million-dollar challenge for AI models to solve five critical problems in materials science.

1:40While 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 and advanced materials. One of the co-founders said, 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. 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 does not 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.

2:30The company is offering a pool of prizes, worth$1 million in total, for solutions to these five problems. 1. A theory to explain cup-rate superconductivity or the ability of certain materials to have zero electrical resistance at ultra-low temperatures. 2. A numerical method to predict the structures of interfaces between silicon and silicon dioxide, a material combination found in almost all semiconductors. 3. A numerical method to map grain structure to thermal properties, enabling the design of better heat dissipation materials. 4. A theory to predict why shining light on materials can change their behavior, particularly around superconductivity.

3:215. A numerical method to improve density functional theory, a key technique used in materials science to model the structures of atoms and molecules. Philippe Hornata, a Stanford professor who developed the challenges, said, Across materials science, there are phenomena we observe but cannot explain. 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 a couple of days.

4:04The models spent several days on each problem without making much headway, though. Still, it's easy to imagine open AI or Anthropic attempting to one-shot them with significantly more compute or a newer model. The challenge also reflects the changing nature of scientific inquiry, as AI proves capable of uncovering insights and techniques human researchers did not anticipate. With solutions apparently easier to find than ever, the questions become more important. Discover Materials' main 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.

4:46When the company announced its seed round back in August, it hoped that they would generate a useful material within a year. Those timelines have accelerated a little bit, but the company is more confident that they'll be able to find these industrially useful materials in a timeline shorter than a year.

5:11Nominal is a four-year-old Austin-based startup that offers an AI platform purpose-built for engineering and testing complex hardware like aircraft and nuclear reactors and smart factories. The company has raised another big round of financing that triples its valuation. On Monday, it announced a$240 million Series C at a$3 billion valuation led by Lightspeed Venture Partners and co-led by Insight Partners. This follows the company's announcement back in March of a$155 million Series B extension at a$1 billion valuation led by Trey Stevens at Founders Fund. That round was a preemptive deal, meaning an offer initiated by the investor, not the startup.

5:58Other investors in the company include Sequoia, General Catalyst, and Lux Capital. Nominal says that it has 80 commercial and government organizations as customers. One of its key customers is Anderil, the defense company co-founded by Stevens. Nominal also won a government contract in October when the Air Force selected it for a$71.2 million strategic funding increase investment, which is a program that requires matching funds from outside the Air Force. Nominals CEO and co-founder Cameron McCord is a former U.S. Navy submarine officer and an Anduril alum. That's all for now. For the latest in startup tech news, go to techcrunch.com.

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