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

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Why is materials discovery harder than biological sequence prediction?

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.

Joseph Krause 0:39

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 (Host) 0:52

Welcome to Latent Space. I'm Brandon. 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 always 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.

Joseph Krause 1:57

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 in material science, particularly in organic 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.

Joseph Krause 2:34

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 so that it can learn and actually predict materials that are relevant to industry. That is what our whole company is built around. And for the last two and a half years, that's what we focused on building.

Brandon (Host) 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 it 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, you know, does it have some cool properties in the lab? But also after that, you know, 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 performances? And so, yeah, you can generate a new composition, and that's very important to do, and we do do that with AI.

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Chapters

5 chapters

1 Why is materials discovery harder than biological sequence prediction? 0:00–4:31 2 What makes Radical AI’s self-driving lab (SDL) different from an automated lab? 4:31–11:12 3 How does Radical generate, synthesize, and characterize new alloy candidates? 11:12–21:55 4 What manufacturing, qualification, and supply-chain barriers block materials from scaling to industry? 21:55–1:16:06 5 How are humans kept in the loop to train the AI scientist and validate experiments? 1:16:06–1:16:43

Speakers

2 identified

Joseph Krause 61 min Brandon (Host) 5 min

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Topics in this episode

Meta 1 Microsoft 1 ChatGPT 1 SpaceX 1 Claude 1 iPhone 2 FAA 1 TSMC 1 DeepMind 2 ASML 1 Starship 1 DARPA 2

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Recorded17 Jun 2026

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Show notes

On the Science pod, we’ve been covering a lot of the ground on how AI is revolutionizing STEM, but one of our favorite off the record topics since our launch is which field is harder to accelerate: math, bio, or physics? Today we’re back in Materials Science land with Radical — Unlike biological molecules that can be represented (and predicted!) by token strings, the success of materials involve many more macro complex variables like supply chains, microstructures, and manufacturing processes. If you recall the LK99 drama of 2023, while the basic ingredients were known, part of the confusion came from the lack of disclosure around manufacturing, and therefore defeated reproducibility. There is probably no "one-shot" model capable of designing a material that works perfectly at scale.How Radical is accelerating materials discovery 10x the pace of DARPA/GE MACHJoseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it’s one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:“We count it as a discovery when you pick up your phone and there’s a new material sitting inside of it.”How does Joseph plan on accelerating the rate of discovery? To understand this, it’s important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.The Self-Driving LabThis philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, but in fact uses an “AI scientist” that combines scientific knowledge, computational techniques, and human intuition to generate and test hypotheses in an automated lab. Creating an AI scientist was key to making Radical’s self-driving labs work, since Joseph argues that no single AI model can one-shot materials.“In materials, the ground truth is the material itself. You have to be able to test it and characterize it.”Joseph talked at length about the self-driving labs at Radical. Joseph argues that experimental data is the true “moat” in this industry. An SDL functions as a closed-loop system where an AI scientist generates hypotheses, and automated robotics synthesize and characterize materials, running research campaigns in parallel rather than serially. The successes here were both on the automation side and on the science side. Radical has managed to scale their alloy discovery pipeline up to producing and characterizing 1200 alloys in six months — this nearly 10x speedup over the DARPA/GE MACH program that aimed to create 500 new alloys in a year. Joseph claims they can scale this up even more and estimates they can produce a hundred new alloys tested and characterized in a day. A truly new paradigm in high-throughput alloy experimentation.On the science side, their AI scientist proposed and tested 300 new materials, ten of which were found to have novel state-of-the-art properties that are already being further developed for commercial applications. The robustness of this first materials campaign reinforces Joseph’s claim that the moat is the lab and data.“It’s moved into elemental families or alloy families no one has ever published on before.”Interestingly, Radical’s AI scientist has made some novel discoveries, expanding into elements that just were not explored prior. This is fascinating from a scientific perspective, but it’s also important for helping reduce supply chain bottlenecks for vital industries!Joseph spent a lot of time in D.C. before founding Radical, and he’s clear-eyed about the competitive threat. China’s centralized model lets it stand up manufacturing hubs and immediately scale new materials from lab to production. We can’t replicate that, and Joseph is very clear we shouldn’t try. But we do need an answer. For Joseph, that means transforming the scientific workforce, investing in self-driving lab infrastructure at the national lab level, and leaning hard into public-private partnerships.“Now imagine every scientist in the United States doing 10 times the research output. That’s fundamental. That just changes the trajectory of discovery.”Before we close, we’d like to give a shout out to Joseph and Radical for publishing and open sourcing much of their internal tooling pipeline. This includes:* TorchSim (preprint, blog): an open-source PyTorch-based MD simulation framework, which has been spun off into its own non-profit.* MATRIX/MATRIX-PT (preprint, blog): An open-source dataset for benchmarking autonomous self-driving labs (MATRIX), along with with an open source model based upon this dataset (MATRIX-PT). We could talk about this extensively, but a fun data point is that improving reasoning in the area of materials also improved reasoning for biological systems! This is a truly unexpected result.Big shout-out to the Radical team for sharing their work!Materials discovery has been stuck on a 20–30 year timeline for generations. Joseph thinks that’s about to change, and Radical AI is putting that thesis to the test in the lab, one sample at a time.We had a great time talking with Joseph. We hope you give it a listen!Timestamps* 0:00 Introduction to the challenges of AI in material science* 0:52 Welcome and introduction to Joseph Krause and Radical AI* 1:38 Why Radical AI is different: The focus on experimental data and Self-Driving Labs (SDLs)* 6:19 The process: Candidate generation, synthesis, and characterization* 11:05 The application of exotic alloys in extreme environments (aerospace and defense)* 13:20 Barriers to entry: The slow process of qualification and manufacturing* 16:06 Supply chain constraints in material science* 19:24 Human-in-the-loop: Training the AI using scientific intuition* 20:35 The engineering challenges of automating a laboratory* 23:17 Defining the “Self-Driving Lab”: Research campaigns vs. just automation* 24:39 Mechanical challenges: Handling high-temperature samples* 27:41 Future scaling plans and the “Vertical Integration” strategy* 30:08 Validation timelines for high-tech industries (semiconductors, aerospace)* 31:47 The active learning loop and handling “negative results”* 35:32 AI exploring elemental families beyond human bias* 39:13 Throughput targets and the difference between AI and human exploration* 43:52 Why the dataset size is less critical than the quality of experimental feedback* 46:20 Addressing the lack of an “AlphaFold” for materials* 53:49 War stories from the lab: Building the infrastructure* 58:12 The shift in industry sentiment toward SDLs and tool interfaces* 1:01:14 Geopolitical considerations and the race in material science innovation* 1:06:12 Calls to action for ML and AI engineers: Rethinking the scientific stack* 1:09:53 The Matrix model and using VLM for scientific knowledge extraction* 1:13:10 Why Radical AI is open-sourcing their work This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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