Inside a $300 million bet on AI for physical R&D

Better AI and smarter robots could mean better physical science research, including for materials discovery. The co-founder of Periodic Labs explains how.

An episode of the Catalyst with Shayle Kann podcast, hosted by Latitude Media, titled "Inside a $300 million bet on AI for physical R&D" was published on November 6, 2025 and runs 36 minutes.

November 6, 2025 ·36m · Catalyst with Shayle Kann

0:00 / 0:00

Summary

A big problem with using artificial intelligence to discover new materials? It struggles to predict beyond its training data. That means AI might be better at optimizing known materials than discovering entirely new ones — like a room temperature superconductor or carbon-capture sorbents. But since we last covered the topic in September 2024, a few things have changed. OpenAI released its powerful O1 reasoning model. Large language models have also gotten better at math, physics, and coding. And lab automation — robots mixing liquids and powders, running characterization tests — has improved, allowing for a higher volume of experiments. So, can these improvements overcome AI’s training data problem? In this episode, Shayle talks to Ekin Dogus Cubuk, cofounder of Periodic Labs, which raised $300 million seed round in September. Last year, Dogus took a more cautious view on using AI for materials discovery. Now though, he’s convinced there’s a clearer path forward for physical science research and development, especially materials discovery. Shayle and Dogus cover topics like: Creating experimental and synthetic data to overcome AI’s limitations of predicting beyond its training set Why we should focus on breakthrough discoveries over easier, incremental wins The different roles humans and AI play in the discovery process Period’s focus on automated experimental labs using AI-generated hypotheses Resources: Catalyst: Can AI revolutionize materials discovery? Latitude Media: This ‘superintelligence platform’ just raised $200m in seed funding Latitude Media: Can AI get us closer to fusion? The New York Times: Top A.I. Researchers Leave OpenAI, Google and Meta for New Start-Up Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is our executive editor. Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com. Catalyst is brought to you by Bloom Energy. AI data centers can’t wait years for grid power—and with Bloom Energy’s fuel cells, they don’t have to. Bloom Energy delivers affordable, always-on, ultra-reliable onsite power, built for chipmakers, hyperscalers, and data center leaders looking to power their operations at AI speed. Learn more by visiting BloomEnergy.com. Catalyst is supported by Third Way. Third Way’s new PACE study surveyed over 200 clean energy professionals to pinpoint the non-cost barriers delaying clean energy deployment today and offers practical solutions to help get projects over the finish line. Read Third Way's full report, and learn more about their PACE initiative, at ⁠www.thirdway.org/pace⁠.