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 measurement services. [LINK]

The Materialism Podcast is sponsored by Materials Today, an Elsevier community dedicated to the creation and sharing of materials science knowledge and experience through their peer-reviewed journals, academic conferences, educational webinars, and more. [LINK]

Thanks to Kolobyte and Alphabot for letting us use their music in the show!

If you have questions or feedback please send us emails at materialism.podcast@gmail.com or connect with us on social media: Instagram, Twitter.

Materialism Team: Taylor Sparks, Andrew Falkowski, & Jared Duffy.