About this episode

The episode discusses the development of self-driving labs in materials science with Joseph Krause from Radical AI.

What if a materials lab on Earth could screen a hundred alloys a day with little input from the scientist? Taylor and Andrew sit down with Joseph Krause, CEO and co-founder of Radical AI, to dig into what it takes to build a self-driving lab and why most of the field is still missing the hard part. From discovering that flagship SEM and XRD instruments ship with no real data access (and rebuilding their entire OS around the workaround), to MATRIX — their multimodal vision-language model that hones in on a target property in roughly 20 experiments — Joseph walks through the technical bets that got them here. He explains why they're using language-model embeddings to teach Bayesian optimization what "28% titanium" actually means, why "scientific intuition" has to be measured as a delta between human and AI annotations, and why Radical is going all the way to manufacturing instead of licensing compositions — because the real IP, and the only training data that matters, lives on the production floor. Check out Radical AI here [LINK] This episode of the Materialism Podcast is sponsored by Momentum Transfer. Visit their website for more details about their…

People in this episode

Hosts: Taylor Sparks, Andrew Falkowski

Guest: Joseph Krause

Sponsors

Momentum Transfer, Materials Today

Mentioned in this episode

Organizations: Radical AI, Kolobyte, Alphabot

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