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It’s hard to believe that Periodic was only launched last September: @periodiclabs. Our mission is to accelerate science.\n\nOur founding team co-created ChatGPT, DeepMind’s GNoME, OpenAI’s Operator (now Agent), the neural attention mechanism, MatterGen; have scaled autonomous physics labs; and have contributed to important…","username":"periodiclabs","name":"Periodic Labs","profile_image_url":"https://pbs.substack.com/profile_images/1970329644709847040/bZtFlIQW_normal.jpg","date":"2025-09-30T16:05:07.000Z","photos":[],"quoted_tweet":{"full_text":"Today, @ekindogus and I are excited to introduce @periodiclabs.\n\nOur goal is to create an AI scientist.\n\nScience works by conjecturing how the world might be, running experiments, and learning from the results.\n\nIntelligence is necessary, but not sufficient. New knowledge is","username":"LiamFedus","name":"Liam Fedus","profile_image_url":"https://pbs.substack.com/profile_images/946842870916448256/X0_3p45X_normal.jpg"},"reply_count":51,"retweet_count":99,"like_count":684,"impression_count":242847,"expanded_url":null,"video_url":null,"video_preview_media_key":null,"belowTheFold":false}" data-component-name="Twitter2ToDOM"> 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 pre [truncated for AI cost control]