Material Abundance: Radical AI’s Closed-Loop Lab Automates Scientific Discovery

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today, my guests are Joseph Krause and Jorge Colindres, co-founders of Radical AI, a company that's building a materials flywheel, an integrated system combining frontier AI models with almost fully autonomous laboratories to dramatically accelerate the discovery and production of new materials. As someone who spent a year as an undergraduate research assistant, weighing out small amounts of fine powders and running very tedious experiments, I have long dreamed of the day when science would start to be automated. That day, I'm pleased to say, is now arriving. Radical AI's lab can run 100 experiments per day, roughly doubling both what I did in a full year as an undergrad and what Joseph did in a full year at the Army Research Lab. And they're using that unprecedented throughput to tackle some of humanity's most important materials challenges. The upside here is truly enormous. As Joseph and Jorge explain, advanced materials are the foundation of nearly every major modern industry, from semiconductors and aerospace to energy and defense. Yet developing new materials remains painfully slow and expensive, typically requiring over a hundred million dollars and more than a decade to go from discovery to commercialization. This creates a so-called valley of death between academic research, which focuses on fundamental understanding, and corporate R&D, which for the most part, focuses on incremental improvements. In that gap lie potentially transformative materials that could enable futuristic technology, such as floating trains, interplanetary travel, and hypersonic flight, but which nobody has successfully brought to market. Joseph and Jorge walk us through their entire closed loop system, how they use property-driven optimization to navigate the vast space of possible materials, how their AI engine incorporates multimodal data from papers to microscopic images, how their robotic lab conducts synthesis and characterization, why they believe that capturing experimental data at scale is the key unlock, and crucially, how they are vertically integrating all the way through to manufacturing. Along the way, we also discuss why material science has become more expensive over time, the LK99 saga and what it reveals about the challenges of synthesis and reproducibility, how the Radical AI system learns from both successes and failures, ultimately building toward a sort of scientific intuition, and their recent Air Force contract to develop high entropy alloys for hypersonic applications. What struck me most about this conversation is the scale of Radical AI's ambition. This is not a company trying to make paint a bit more durable. On the contrary, they are trying to create the materials that will define entirely new industries.
One quick note before we get started. For those who want more on the machine learning of material science, my previous episodes with Tim Deignan and Jonathan Godman of Orbital Materials could be great compliments to this conversation. Today we focus primarily on the discovery engine that Radical AI is building at a system level. But those earlier episodes do go much deeper into the technical details of how AI models learn to simulate molecular dynamics and ultimately predict material properties. Of course, while the two companies could naively be viewed as competitors, I think this conversation makes it abundantly clear that there is functionally unlimited opportunity for better living through material science. And it's certainly my hope that both companies will go on to massive success. With that, I hope you enjoy this conversation about accelerating materialist discovery with a vertically integrated combination of AI and automation. With Joseph Krause and Jorge Colindres, co-founders of Radical AI.
Joseph Krause and Jorge Colindres, co-founders of Radical AI. Welcome to The Cognitive Revolution. Thank you.

**Joseph Krause** (3:51)
Thanks for having us. I'm excited to be here.

**Nathan Labenz** (3:53)
Yeah. Likewise. So you guys are doing some really interesting frontier work in the application of AI to material science. And there's a lot to unpack. We were just joking beforehand that automating science is something that as an undergrad research assistant, I dreamed of for many hours as I was sitting there doing the tedious work of weighing out small amounts of fine powder. And that is, I was told at the time that that was very difficult to automate and probably wouldn't happen in my lifetime. So the fact that it is starting to happen as, you know, obviously part of a bigger vision gets me excited right off the bat. For starters, you know, our audience is very interested in AI by definition, probably mostly doesn't know a ton about material science broadly. So maybe you could kind of just set us like a baseline. Why is material science hard? You know, what's going on? One of the observations, you know, from kind of doing my homework on the company is that the cost of material science breakthroughs seems to be going up. So what's the kind of lay of the land today that you guys are entering into with a new paradigm? Yeah.