Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk

It’s hard to believe that Periodic was only launched last September : One year later, it is considered one of the pre-eminent AI scientist labs, with dizzying talent density and astonishing progress in the autonomous lab buildout: Most people are familiar with the standard credentials of Liam and Dogus , but we found an incredible “talent slope” while learning more about Periodic, where each successive employee seems more impressive than the last: From building AI systems that reason over noisy physical experiments to creating laboratories where every instrument can become intelligent, Periodic Labs is betting that the next frontier of AI won’t come from simply training on more internet data , it will come from letting models experiment with the real world. In this episode, Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk join swyx and Brandon to explain why scientific discovery is fundamentally different from math and coding, and what it takes to build AI scientists that can actually discover new materials. We go deep on Periodic’s vision for “synthesis superintelligence” : reinforcement learning grounded in physical experiments, AI-powered materials characterization, simulations and density functional theory, high-throughput labs, and systems that learn from the entire process of doing science rather than only its published results. Liam and Dogus also explain why frontier models will still need experiments , why failed experiments may be some of the most valuable training data, what it means to give every piece of lab equipment “140 IQ,” and how autonomous experimentation could compress decades of scientific trial-and-error into months. We discuss: * Why intelligence alone isn’t enough for scientific discovery * How reinforcement learning changes when the environment is the physical world * Why science requires reasoning under uncertainty, noise, and missing information * Prediction, synthesis, and characterization in the materials discovery loop * Why physics and materials science are still far from “solved” * The “matter compiler” and Periodic’s goal of synthesis superintelligence * Phase transitions, X-ray diffraction, and AI-powered materials characterization * DFT, simulations, and why experiments remain the ultimate ground truth * Room-temperature superconductors, new magnets, batteries, and more efficient compute * Why quantum computing may not automatically solve materials discovery * What it means for every piece of lab equipment to have “140 IQ” * Why data quality and negative results matter more than simply throwing more compute at science * Training models on the process of doing science rather than the final answer * Why even future frontier models will still need to physically experiment * Scaling autonomous labs across AI, chemistry, physics, and custom hardware * How automated experimentation could massively increase the “surface area for luck” in discovering new materials Liam Fedus * LinkedIn: https://www.linkedin.com/in/liam-fedus-26547811/ * X: https://x.com/LiamFedus Ekin Dogus Cubuk * LinkedIn: https://www.linkedin.com/in/ekin-dogus-cubuk-9148b8114/ * X: https://x.com/ekindogus Timestamps 00:00:00 Introduction 00:02:49 AI and Reinforcement Learning in the Physical World 00:09:17 The End-to-End Materials Discovery Loop 00:13:28 Why Physics Isn’t “Solved” 00:19:04 The Matter Compiler and AI Characterization 00:30:22 DFT, Simulation, and Experimental Ground Truth 00:42:27 Dream Materials, Compute, and Quantum Computing 00:45:57 Giving Every Lab Instrument “140 IQ” 00:50:02 Automating the Lab 00:53:16 Data, Models, and Negative Results 00:58:13 Training AI on the Process of Science 01:00:20 Why Frontier AI Still Needs Experiments 01:06:06 Scaling Autonomous Labs 01:10:45 Building the Team and Deploying to Industry 01:17:01 From AI Copilots to Scientific Outcomes 01:20:57 Automating the Search for Superconductors Transcript Introduction: Intelligence Must Meet Reality Swyx [00:00:00]: Okay. We’re here at Periodic. We’re here today with Liam and Doğuş from Periodic. Welcome, and thanks for having us at yours. Liam Fedus [00:00:11]: Yeah, great to be here, and yeah, thanks for coming in. Swyx [00:00:13]: I want to start off with one of these quotes that I love from your website. It says, “Intelligence is necessary but not sufficient. New knowledge is created when ideas are found to be consistent with reality.” It seems so straightforward, but why is it non-obvious? Liam Fedus [00:00:29]: I think that’s sort of the thesis and premise behind Periodic that Doğuş and I thought when we were building this, which is you can’t just think your way to a solution. The universe is so complicated that in order to actually push the frontier of knowledge and to make progress, you need to create these conjectures and then actually see whether or not it holds. No amount of rereading that textbook or paper, thinking, or disappearing into a room is going to allow you to think through all possible experimental outcomes, expected and unexpected, and you really need this iterative process. And that’s our core belief for putting this together, and that’s why from the very beginning, we’re like, “We need to have the AI systems, the simulations of the physical world, but then also to build up the physical high-throughput experiment.” And we think a different type of intelligence emerges from that. Swyx [00:01:21]: I think it’s also notable, like with OpenAI and Google, you don’t have those resources in those big labs. You had to come out and do your own thing, because you’re kind of inventing your own playbook as you go along. Building an Interdisciplinary Lab for AI Science Ekin Doğuş Çubuk [00:01:34]: Yeah, and a team like this has never existed, I think. We try to bring together solid-state chemists, solid-state physicists, experimentalists, theorists, hardware engineers, LLM experts, computer scientists, and some of these technologies are very recent. The high-throughput experimentation, the robotic arms have only been tried in the last three years or so. The force field expertise, some of these force fields are also very recent. But yeah, we felt like having a lab is very important. Having this focus and bringing all these people together to work together is very important. And I guess the closest examples from history are places like Bell Labs, where incredible theorists and experimentalists and chemists worked together and achieved incredible things. So we are trying to do the same here, yeah. Physical-World Reinforcement Learning and Uncertainty Brandon [00:02:17]: I want to talk about force fields in a bit, but Swyx [00:02:19]: Yes Brandon [00:02:19]: Before we get to that. Yeah what are they? But before we get to that, For context, some of our audience are AI engineers, and some are scientists. For the engineers: what’s the difference between working at a big lab like OpenAI and what you’re doing here? I think you’ve already hinted at this but maybe explicitly, how do you have to reframe your thought process? Liam Fedus [00:02:49]: Well, I think one interesting thing is now our reinforcement learning environments literally derive from the environment, from our physical labs. Our data comes from our physical labs, and this is sort of our ultimate truth. It’s not enough just to do optimization against, some answers that were known in some papers or textbooks because we’re going beyond that, and I think that’s one of the biggest differences. But along with that, there’s issues of say, decision-making under uncertainty. So when you’re doing optimization against math, there’s a high precision to it. You’re not really dealing with kind of like variance or uncertainties or aberrant measurements, whereas that’s very key to our process. For example, when we are doing a materials discovery loop, things don’t come out of the furnace labeled, right? Even the labeling process can be stochastic and noisy. And sometimes you’ll have aberrations between different machines. Maybe we’ll have insufficient telemetry. So we thought we ran it at this temperature, but really the temperature was, some delta away. And an intelligence that can take in this noisy data and make intelligent decisions like a scientist would, is a sort of a different set of reasoning strategies. So I think there’s a huge amount of commonalities, so the standard mid-training, reinforcement learning, the construction of tool-using agents, variance reduction, making sure infra doesn’t have mismatches between training and inference. But we have to go beyond that and really think about how do you do accurate, work when there’s a high amount of uncertainty? How do you make really incredibly efficient use of a limited set of data? So we’re scaling things up significantly, but still, it’s very different than a digital environment where you can arbitrarily add more environments or more rollouts. We don’t have that same capacity. So sample efficiency is another key thing. Why Experiments Are Noisy and Incomplete Brandon [00:04:48]: This seems a little bit abstract to me, and it is a theme which has been on the science pods for a bit, but I think it might be helpful when you talk about experimental uncertainty, to explain what would a specific process that you were doing experimentally look like, and how would a human approach this problem, before we get into the AI side? Do you have a specific example of a type of material you make and what sort of uncertainty in the measurements you would experience in the process? Ekin Doğuş Çubuk [00:05:17]: I think there are many dimensions to this. So one of them is I think every scientific experiment has to do some dimensionality reduction, and this is very different than coding or math. So, when you’re doing math or coding, all the context could be available to the human or the LLM. It’s a bunch of axioms, some corollaries people have derived from them. It’s a bunch of functions and API calls. So everything you need to reason through is available to you, and then you just have to be very smart and figure it out. In physics, as you know, we start with more atoms than we could ever store on a computer. So clearly, we’ll have to go from the original number of dimensions and bits that represent a system to the number of bits we can fit in the computer. And this was, I guess, the original premise of thermodynamics. They were originally really confused about how steam engines worked, but then turns out you can find five or six thermodynamic variables that explain what’s going on pretty well, which is incredible. That’s the beauty of physics. Brandon [00:06:13]: You have a room full of atoms, and there are 10²³ or 10²⁷ Ekin Doğuş Çubuk [00:06:19]: Exactly Brandon [00:06:19]: Or something atoms in this room, and yet you need temperature and pressure and simple variables, and that tells you everything. No, it tells you mostly what you need Ekin Doğuş Çubuk [00:06:28]: A lot Brandon [00:06:29]: To know for most cases, right? Ekin Doğuş Çubuk [00:06:30]: Yeah. Brandon [00:06:30]: So let’s say you make a material, and you could do something like look at an X-ray structure, and this thing is a very lossy, projection, right? It’s not actually a structure, or it doesn’t tell you the true structure. It tells you some piece of principal components of that. So, how would a human take these simple principal components and then, kind of reason about them? And then how do you extend that to an AI? Ekin Doğuş Çubuk [00:06:58]: So on the theoretical physics side, right, we’ve decided that energy, the average value of energy and the fluctuations of energy is very important. So a lot of thermodynamics has been derived from this. And then we realized reaction barriers are also very important. The kinetics is very important. So a human can try to reason through this extremely complex system by coming up with some, reduced dimensionality descriptors. People also really like atomistic picture. You think about what the local neighborhoods look like, whether they’re from octahedra, tetrahedra, and that especially chemists really benefit from that. Okay, so that’s on the theoretical side. And on the experimental side, you would try to collect as much data as possible. So you’d have a lab notebook, and you’d write everything you see, et cetera, but with the understanding, of course, that you’ll miss a lot. So like as Liam mentioned, there are all these issues that come up. For example, your furnace will degrade over time because as you’re using the furnace, some of the things you’re baking will evaporate and cover the heating element. So every time you use the furnace, it gets worse and worse. Another issue is you have a furnace, but the temperature isn’t perfectly uniform, so where you put in the furnace will affect the result. We don’t have this issue yet, but we will maybe someday. The optical instruments, suffer a lot from vibrations. So every time somebody walks. I remember when I was doing my PhD, one lab had this big issue because sometimes their results would be very different than other times, and they brought it down to at a certain time in the night upstairs, somebody would be walking and that vibration affected the laser setup. So yeah, science is very hard, but that’s what makes it special, right? The other thing Liam and I have been talking about is a lot of the current improvements focus on math and coding and theoretical computer science because it’s easier for LLMs. But in real life, most things that require intelligence are actually more like science. There’s a lot of uncertainty, there’s a lot of noise, there’s a lot of missing context, but you have to be the intelligent being and figure out what to do next. Liam Fedus [00:08:53]: I think a really interesting point to build on that too is the optimal reasoning strategies to do math, to do theoretical computer science, to do these types of things may not be the optimal reasoning strategies to do science. Ekin Doğuş Çubuk [00:09:04]: Yeah. The Materials Discovery Loop: Synthesis and Characterization Brandon [00:09:05]: How do you define an RL reward function when your output is a noisy crystal structure or some number of I don’t know collective variables or something? Like. Liam Fedus [00:09:17]: Maybe, I’ll give, one part of the loop, and I’ll just kind of dig into that. So, as part of the materials discovery loop, we have to first figure out, what to make. So that’s the stability of the atoms together, as well as do we expect it to have the properties we want. So we don’t just want, a novel configuration of the atoms. We want them, when put together, to have the property of interest. Next is how do you synthesize it? How do you actually make the thing? That’s also non-trivial. So even if you know something will hold together, the process of actually, what are the processing conditions to actually bring that forth is highly non-trivial. But again the thing. Once you’ve done those two steps and you’ve made something, it doesn’t come off labeled, so you actually have to characterize it and figure out what you made. So in that case rather than thinking about the RL environment of we’re just going to kick off an experiment, wait a couple days, and try to do a update on did you find a room-temperature superconductor or not, that’s completely infeasible. Brandon [00:10:15]: Yeah. I was going to ask that. Yeah you can’t leave your GPU sitting for a week or something. Liam Fedus [00:10:19]: It’s. Yeah, it’s completely infeasible Brandon [00:10:20]: Yeah Liam Fedus [00:10:20]: Because you don’t have enough agents to reduce the variance sufficiently. The rollout time is going between the different instruments. You’re waiting. There’s just some physics limits of the experiments, for the actual synthesis or furnace time. So it’s just too slow, too noisy. So the way we think about the AI program is basically constructing agents around this set of data that we’ve produced. And so in the case of characterization, you’re looking for RL environments that are rewarding the identification of the phases actually present. Are you able to given the raw experimental data, fit that effectively? So the way we do this is we’ll shoot X-rays, at the material. X-rays have a frequency or a wavelength roughly measurable or commensurate to the spacing between atoms, so you get nice diffraction patterns that kind of give a fingerprint of the crystal structure. And doing this, identification of what actually is present is highly non-trivial. And so some of our early AI work has been on basically producing systems to do this. And this allows us to move much more quickly too, because if you’re running so many experiments, you really quickly become bottlenecked on your ability to understand what was produced. But again, now the reinforcement learning environment is fairly straightforward, which is you’re going to reward the identification of the phases present. You’re going to penalize spurious phases or things that don’t have any real chemical plausibility. So that’s like a little succinct thing where you can kind of do this for the different pieces. And when you stitch this together, that’s the end-to-end discovery loop. Training on Novel Experimental Data, Not Memorized Answers Brandon [00:12:02]: Okay. You Liam Fedus [00:12:02]: Maybe another Brandon [00:12:03]: Yeah. Oh, sorry Liam Fedus [00:12:03]: Another piece would be as we build up, a large basket of experimental data, you can think of another thing of basically timestamping the state of the world. So you could say, “At this date, this was our experimental evidence up until that point.” And you can construct reinforcement learning environments where you say, “Okay, given that body of experimental evidence and given, the choices, what was the next choice that the scientist made, or what was an outcome of that experiment?” And. This is really interesting too, because this allows us to do types of programs that would be harder externally. Because if the pre-trained model has already memorized some of this data and then you try to do reinforcement learning on that, if it already knows the answer and you construct reinforcement learning tasks that depend on that answer, it can fake work. So, and then, because it’s like, oh, well, it already knows the answer, so it doesn’t actually have to do the hard physics reasoning. It doesn’t have to actually do the calculation simulations. And it gets to the right answer, and then you reinforce that policy and you up-weight those reasoning strategies. Those reasoning strategies will not generalize to novel systems, so it’s not useful to us. But as we build up baskets of experimental data and stitching things together, having the lineage through the whole process, that gives us the ability to construct new types of environments that are just not plausible elsewhere. Why Known Physics Does Not Mean Perfect Simulation Swyx [00:13:27]: I want to ask my question Brandon [00:13:28]: Seriously Swyx [00:13:28]: But I’m worried that I’m taking it off the rails. I’m just going to go for it because I, again, I come from the non-scientist, but engineering sort of. I’m familiar with the ML side, but not the not the physics side. Okay, there’s a few versions of this, but I think the basic question is, don’t we have most of the laws of physics worked out? And how come we don’t have perfect simulators already? Brandon [00:13:50]: Oh, that’s a great question. Swyx [00:13:52]: Like, this is very basic, like. And he was like, “Ha, that’s cute.” But like Brandon [00:13:56]: No, it’s a great question Swyx [00:13:57]: Dude like I have all these physics books. What do you mean, what do you mean we’re not done? Ekin Doğuş Çubuk [00:14:03]: Yeah, so there are a couple of things going on here, right? So Swyx [00:14:07]: Yeah Ekin Doğuş Çubuk [00:14:07]: I t