🔬 The Self-Driving Lab — Joseph Krause, Radical AI

**Joseph Krause** (0:00)
This is the difference between AI for bio and AI for materials. If you look at bio or maybe small molecules as a more broad category, you look at selfies and smile strings, right, which has been a big way to have those materials, those molecules in text. And then you can use that, and that's because you know the elements, and then you know the bonds, and so you know most of the things you need to know. But what about everything I just told you about the alloy? Supply chain, cost, microstructure, how you're processing, additive versus casting, how do you capture that in a string? You can't. And this is what's so hard is there is no one model that can one-shot a new material that ends up in your iPhone, or that ends up on Starship. That's just not the way materials work. And so there is this really tough challenge of how do you capture all this data and try to bring that back and kind of really improve your AI engine to encompass more than just discovery.

**Brandon Anderson** (0:52)
Welcome to Latent Space. I'm Brandon.

**RJ** (0:54)
I'm RJ, and we are in the room with Joseph Krause, CEO of Radical AI. Joseph, you're in a market that's getting crowded really fast. You have Lila, you have Cusp, you have Periodic, all developing AI for materials, something, something. What are you trying to do that's different?
And how are you going to beat the heavily capitalized competition?

**Joseph Krause** (1:20)
Guys, great to be here. Thank you so much for having me. And I must start with big fan of the show. I got to commute in the New York City every day, and you're one of the top things that's in my rotation. I love learning and I'm a material scientist by training. And so the aspects that I can learn from your show, awesome. So super excited to be here, especially in person. Thanks for making it work.
What makes us different is our deep belief in experimental data, right? And I think now you're starting to see the industry pay more attention to this. And you see self-driving labs. I talked about concept everywhere from academia to people like Google DeepMind all the way through to pretty much every competitor that you've named in the space building an SDL. It was not always that way.
When we started the company two and a half years ago, people thought we were crazy. That's CapEx intensive. Are you really going to be able to pull the data? Models aren't really built for that data today. And we can get into why models struggle on material science, particularly inorganic material science specifically. And so why are you going to do that? And I had a deep belief, my co-founders had a deep belief that, well, in materials, the ground truth is the material itself. You have to be able to make it, you have to be able to test it and characterize it. And then you have to really at one point be able to see if it can go into a real application, if you're going to have it used in industry. And that was where our thesis really started from was, you're going to build this loop, this closed loop system, what we call a self-driving lab, that can actually run those experiments, capture that data and feed that information back to your AI scientists that it can learn and actually predict materials that are relevant to industry. That is what our whole company has built around. And for the last two and a half years, that's what we focused on building.

**RJ** (2:57)
Great. So why? Why do you believe that versus the, pejoratively, the think big thoughts and then come up with stuff and then try later?

**Joseph Krause** (3:05)
Yeah, because so much of what makes a material real is in the latter part of the discovery process. And I mean specifically at the characterization and synthesis phases. So, hey, what did we make and does it have some cool properties in the lab? But also after that. We work in a field called structural metals or alloys.
So much of what dictates the performance of those alloys is actually in processing. How do you manufacture it? What techniques are you using post-processing and manufacturing that push performance or change performance? And so, yeah, you can generate a new composition, and that's very important to do, and we do do that with AI. But it's everything that comes after that that actually impacts if you have a new discovery, if that new discovery is relevant to the application space you're going for, and then can you actually make it? And can you scale it? Can it actually go into the application space and be used by an end customer?