Synthetic Data Versus Experimental Value

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. 

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