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

15 snips

Nov 6, 2025

Ekin Dogus Cubuk, co-founder of Periodic Labs and former Google DeepMind researcher, dives into the exciting intersection of AI and materials discovery. He reveals how advancements like OpenAI's O1 model are helping to tackle AI's limitations in predicting beyond its training data. The conversation shifts to the importance of breakthrough discoveries versus incremental ones. Ekin also discusses the synergy between human insight and AI automation, and how Periodic focuses on automated labs to refine experimental hypotheses.

Ask episode

AI Snips

Chapters

Transcript

Episode notes

INSIGHT

LLM Reasoning Unlocks Beyond-Training Exploration

Advances in reasoning LLMs (like O1) plus test-time compute let models act beyond limited training distributions.
Combined with high-throughput experiments, this enables iterative discovery loops that can find novel materials.

INSIGHT

Closed-Loop AI+Lab Iteration

Periodic connects LLMs to lab automation so models propose experiments and immediately receive results.
The loop lets the model tweak experiments iteratively using both literature context and novel experimental outputs.

ADVICE

Automate Synthesis And Characterization

Automate as much of synthesis and characterization as possible to increase trial throughput.
Prioritize improving automated characterization next because it's currently the bottleneck for scaling experiments.

Get the Snipd Podcast app to discover more snips from this episode

Get the app

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.