合成超级智能：从半导体到超导体——Periodic Labs 的 Liam Fedus 与 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 think first of all, we’re definitely not done with any of the laws of physics. Even the

Swyx [00:14:12]: At quantum scale, okay, but. And maybe very large scale, but at human scale, at material scale, we’re, we. What’s left?

Ekin Doğuş Çubuk [00:14:22]: We’re not. So it’s a it’s a really good question. And it’s very interesting why we’re not done yet, right? So there’s a very, common story people talk about. When Dirac was figuring out quantum mechanics, like late 1920s, he wrote a textbook about quantum mechanics, and he kind of framed it as it’s all done. And then there’s a phrase people like to quote. I’m not sure actually how accurate it is, but apparently Dirac said, “The rest is chemistry.” The idea being he could solve the hydrogen atom

Swyx [00:14:50]: Just compose everything.

Ekin Doğuş Çubuk [00:14:51]: Yeah. He could maybe solve a chain of 1D hydrogen atoms, but then he cannot currently solve, say, nitrogen interacting with oxygen, but that’s okay, that’s just chemistry.

Swyx [00:15:00]: Yeah.

Brandon [00:15:00]: Physics physicists really love single atom rep. Really simple systems.

Ekin Doğuş Çubuk [00:15:05]: Simple systems.

Brandon [00:15:05]: And that’s all you can solve.

Ekin Doğuş Çubuk [00:15:07]: Spherical cow.

Brandon [00:15:07]: Yeah, spherical cow.

Ekin Doğuş Çubuk [00:15:08]: But it turns out, I think what we learned in the last hundred years is, first of all, that’s not true. It’s not like a simple extension of what it was to do just hydrogen. And it’s not just. The rest is not just chemistry. There’s actually a lot of physics there. I guess one of the things we still haven’t figured out is high-temperature superconductivity. But there are a lot of other

Swyx [00:15:25]: And high for you is 175, 200?

Ekin Doğuş Çubuk [00:15:28]: Oh, when people say high-temperature superconductivity, what they mean there is unconventional superconductivity. So there’s the conventional superconductivity that’s just mainly driven by electron-phonon coupling. And in that case, you can kind of see the isotope effect. If you take the same system but just different weight for one of the elements, you can see the superconducting temperature drops at the rate you expect. So that one is conventional. But then higher-temperature superconductors like cuprates, like the ones that are above 77 kelvin, like 93 kelvin, turns out don’t obey that physics. But we don’t know what physics they obey. They’re just incredible superconductors. But it’s not just that. So that definitely is one of these very, popular topics we don’t know the theory for yet. But there’s so many other things we don’t understand. For example, even strong correlation in simple quantum mechanical systems, we cannot simulate yet. Density functional theory is a very powerful tool. It’s incredibly accurate on some things, but as long as. As soon as there’s some strong electron correlation, it actually fails to capture some of the effects. There’s so much to figure out. That’s why I’m really excited for AI to be applied to this field, because theoretically, computationally, and experimentally, there’s so much to figure out. And every improvement we make here should improve human life, because, the better we can understand materials, solid-state physics, the better devices we can make for them.

Brandon [00:16:41]: There’s this famous quote by Phil Anderson, which is, “More is different,” which is basically you may understand all the basic laws of something, but when you add many things, they behave qualitatively distinct, differently from how the simple physics should tell you. So understanding how lots of things behave together, it seems like it should be simple. Beginning laws are simple, but the collective behavior is so complicated that it’s really hard to model.

Ekin Doğuş Çubuk [00:17:07]: It’s emergent and universal, which is crazy. And we’re also seeing this with deep learning models, right? We see power laws everywhere in deep learning. We don’t understand, but it is very reminiscent of physics where there is, emergence and universality.

Brandon [00:17:20]: There’s been many theory papers in the physics world about emergence in neural networks.

Ekin Doğuş Çubuk [00:17:24]: Yeah, absolutely, yes.

Brandon [00:17:25]: Yeah.

Synthesis Prediction and the Matter Compiler

Swyx [00:17:26]: And then my other follow-up question was also just on the process of let’s call it the yeah, process and the end result. Let’s call it like, you want to have these properties that you’re targeting for materials, and then you have to figure out the process to get there. Is it worth separating these things so that you can have. You just train a model that perfectly reverse engineers any process whatsoever given, whatever theoretical end state you want? Is that, meaningful?

Ekin Doğuş Çubuk [00:17:54]: Maybe going back to the conversation we had about how since there are more than Avogadro’s number of atoms, we’ll never know the exact context of what happened. I think that might be a reason why we’ll never be able to reverse engineer everything perfectly. But we’re just trying to reverse engineer sufficiently to be able to improve the performance of the materials, basically.

Swyx [00:18:13]: Yeah.

Brandon [00:18:13]: What you’re asking is there a forward model where you put in an input and you can predict the output, and you want to under

Swyx [00:18:19]: Yeah.

Brandon [00:18:20]: You want to create a like a surrogate model which can understand

Liam Fedus [00:18:22]: The input is the final state of that, this configuration of atoms, and then the prediction is

Swyx [00:18:27]: Because then you can, you can split up the work almost. You can have one team do the process side, and then the other team do the property side, and then

Liam Fedus [00:18:33]: Yep

Swyx [00:18:33]: You know, just race them.

Liam Fedus [00:18:36]: Yeah, I think that there’s, a lot of progress we can make in terms of these. Splitting up these agentic workflows to these different areas, I think there’s a lot of empirical work we can make, on synthesis prediction as well.

Swyx [00:18:49]: Yeah.

Liam Fedus [00:18:49]: Whereas taking in that configuration of atoms, what tools can you use? How, given this experimental evidence, given prior literature, prior papers, how do you actually get to these things? And you ultimately are testing, are you able to actually replicate it or not?

Swyx [00:19:04]: The reason I say this maybe also is just thinking about it in terms of a company. You know, in the way that TSMC is kind of like a fab for semis, but they don’t design the semis, you could be the TSMC of materials where people come to you with like, “Well, here’s what I want,” and then you can figure out the process and

Liam Fedus [00:19:21]: Yeah, it’s like a matter compiler.

Swyx [00:19:23]: Yeah.

Liam Fedus [00:19:24]: So I guess given these requirements

Swyx [00:19:26]: Very Star Trek.

Liam Fedus [00:19:27]: Yeah. But it’s like, given these requirements, is this actually a valid set of configurations? Can this, can this exist?

Swyx [00:19:33]: Is it a good objective function? I don’t know. It’s too big.

Liam Fedus [00:19:37]: Yeah. We kind of talk about one of our missions as synthesis superintelligence, so I think it’s in line with this.

Phase Transitions, X-ray Diffraction, and RL Rewards

Swyx [00:19:44]: Yeah.

Brandon [00:19:44]: I want to double-click on something that you said a little while back, which was you talked about phases. And I think this is, going to Doğuş’s comment about, what is emergence. The concept of a phase transition I think might be foreign to a lot of the audience. So can you explain what is a phase transition, and then why is this a helpful signal for something like an RL environment?

Ekin Doğuş Çubuk [00:20:08]: Phase transitions are fascinating. I think the maybe the one that people can most easily relate to in their life is, ice melting probably, or water boiling. You increase the temperature of ice, and it still looks like ice and it behaves like ice, but there’s a certain temperature at which it just stops raising its temperature and then turns into liquid. So it suddenly goes from this solid phase to liquid phase. Another one that’s very relevant to us, of course, is the Ising model. And I think computer science and mathematicians also study this maybe under different names, but basically, you can have spins up and down, and they have different interaction terms if they’re both up versus one is up, one is down. And at high enough temperature, they’re usually just randomly up or down. Entropy wins. You lower the temperature, it’s still looking the same. But then at some point, suddenly they perfectly start aligning. Yeah, the phase transition. Phase transitions are really nice because it makes physicists’ life easier for studying certain things. They tend to have certain, spatial correlations that really help us, as well. Okay, so in our lab, phase transitions come about usually because we mix the precursors that are different, crystals basically, but then we raise the temperature or do something else to encourage them to react, and then atoms start reacting and they form into this new crystal. And the this shows up in the XRD pattern because usually if the geometry of the atoms change drastically, the XRD pattern changes a lot. But what really happens in a typical practical materials discovery campaign is when you first try something, it doesn’t work, and the outcome isn’t just a clear yes or no, but it’s usually a very mixed phase. It will usually have some of the precursors. It might have some amorphous phase, and then it will have a bunch of phases that you maybe didn’t try to make. So that becomes a challenge, and that’s why we find it very difficult to bring together simulations and AI to do the characterization. So the simulations can always say, “Oh, this phase doesn’t look like what we predicted, but this is a slight variation of it. Let me now do force field calculation on it to see if it’s still stable.” Or the AI can say, “Based on the previous experiment and this one, this is probably not the phase we want to make, so let’s change the conditions.”

Liam Fedus [00:22:18]: Yeah, and in some cases

Swyx [00:22:19]: For listeners, XRD is X-ray diffraction, which you already described.

Liam Fedus [00:22:22]: Yeah. In some cases, there’s a phase that just hasn’t been recorded before. It doesn’t exist in any papers or database. And so the AI has to actually go and sort of use these tools to say, “Well, what configuration atoms would actually explain these phases?” But this becomes incredibly important for directing the scientific process because, let’s say we have a phase target in mind. We really need to sort of hill climb that to get better measurements on that. So if it’s, one percent and we’re like, actually, we want to increase this phase purity, we want to have a very good measurement on this experimental data to say, what is present and what’s not.

Brandon [00:22:57]: So one of the advantages is you have just a distinct variable that you can just observe. Going back to the statement about how do you deal with uncertainty, one of your, it seems like part of your answer is you make your observable signature somewhat unambiguous. There’s no interpretation there. Is that right? Is that the logic behind this? Or is this literally you just needed to find a new phase, and so this is the thing you care about?

Liam Fedus [00:23:20]: I think there’s still ambiguity in a lot of these cases. A very dumb thing is replicates.

Brandon [00:23:25]: Yeah.

Liam Fedus [00:23:26]: We run replicates, of course.

Brandon [00:23:27]: Yeah. Yeah.

Liam Fedus [00:23:28]: But no, I think there — You know, it’s a challenging task because it’s not just a fully deterministic, and there are well, there are ambiguities, and other things. Two different, phases can actually be consistent with the same pattern.

Brandon [00:23:42]: Yeah.

Liam Fedus [00:23:42]: And so I think that’s why it’s really important for the system to use some chemical intuition to say “Okay, well, this would be highly unlikely given the synthesis conditions, given this, the prior knowledge.” and so that can be used to help disambiguate.

Chemical Priors, Multimodal Measurement, and Ambiguity

Swyx [00:23:58]: So you’re, so you’re somewhat injecting priors there, based on what you expect.

Ekin Doğuş Çubuk [00:24:03]: Yeah. I think thermodynamics is the biggest prior, right?

Swyx [00:24:06]: It is.

Ekin Doğuş Çubuk [00:24:07]: And then physics is a big prior. Another one that we really benefit from here is multimodality or materials characterization. So we can do XRD, and yes, some phases might look similar on the XRD pattern, but then we can also measure their electrical properties, magnetic properties. We can measure their morphology, like some kind of electron microscopy. And then some of the phases that look similar on XRD will look different in some of these dimensions. And that kind of multimodality really helps. And again, AI is really helpful here because humans have limited context and computation power as well. So if you give humans 10 different modalities at the same time and say, “Analyze these all consistently,” it’s a bit difficult. But it’s actually not super intelligent for an AI to do it. It’s great, yeah.

Liam Fedus [00:24:46]: Yeah, it’s like the super intelligence is it can just do so much calculations. It can look at everything. And this is, kind of gets to that, phrase about better decision-making under uncertainty, where now it’s stitching together, the signal across many different instruments longitudinally, across different experiments, across the replicates, and then kind of getting to this underlying better model

Swyx [00:25:08]: Yeah

Liam Fedus [00:25:08]: Rather than just over-indexing on one measurement from one instrument.

Information Limits, Telemetry, and Maxwell’s Demon

Swyx [00:25:12]: And let me just, like. So you know, you started, I think a while ago also saying that there’s just more data than you can fit in any reasonable computer or process or anything. So at some point, you have to throw out data even though you have all these replicates, even though you have presumably ten different ways to measure the same thing. It’s. ‘Cause what if one of your temperature things is wrong? So when do you throw that out? How do you decide what to do? As a data-hungry ML guy, I just want everything, right? And then I just, you know

Ekin Doğuş Çubuk [00:25:37]: So it’s, yeah.

Swyx [00:25:37]: Waddle, learn everything. I don’t know what I don’t know, and it’s just throw it, throw it, the machine at it.

Ekin Doğuş Çubuk [00:25:42]: Yeah. So we don’t throw any data. I think maybe what I meant was potentially. If God was observing the experiment

Swyx [00:25:50]: Yeah.

Ekin Doğuş Çubuk [00:25:50]: There is more data in the experiment than humans can fit in a computer. Unfortunately, us mortals cannot observe all the atoms. So even though there’s more data than we could store, we actually can’t access that data. For example, I cannot trace all the atoms’ positions. So unfortunately, we’re still in a regime where any data we collect is very precious, and we don’t delete any of it. Yeah.

Brandon [00:26:11]: Maybe a way of thinking of it to connect to the AI side is think about probing, like linear probing of a of a foundation model, right? This model has all this data, so God has this entire picture of what the material is doing, and then the experiment is this little, tiny linear probe which has eight bits or something of output. And so you’re taking this humongous thing and coarse-graining it down to a little bit of information, and then it’s now, either traditionally a human’s job or now your agent’s job to reconstruct from those eight bits of information

Liam Fedus [00:26:43]: It’s an analog, yeah.

Brandon [00:26:44]: What is actually happening inside.

Liam Fedus [00:26:45]: It’s an analog where you can’t have full visibility over every parameter weight, over every activation for that input. Yeah, you’re getting some sort of subset on it or some coarse-grained features across the whole thing.

Ekin Doğuş Çubuk [00:26:58]: Yeah. But so you’ll probably find that. You probably, do find, Maxwell’s demon argument very interesting because that kind of

Swyx [00:27:05]: Can you, can you res-recite it? Because I don’t. He’s mentioned it before. I keep forgetting it.

Ekin Doğuş Çubuk [00:27:10]: So, in the 1800s, people realized that entropy of a system has to increase or a for a closed system

Swyx [00:27:18]: Yeah.

Ekin Doğuş Çubuk [00:27:18]: Or stay the same, but it can’t go down.

Swyx [00:27:20]: Yeah.

Ekin Doğuş Çubuk [00:27:21]: It’s the second law of thermodynamics. And there was a thought experiment that was asking, what would happen if there was a very tiny, all-knowing demon that could kind of look at atoms, and then whenever an atom had low energy, kind of open a door and let it into this other room and keep doing this to reduce entropy of the system, which would break second law of thermodynamics? And for a long time, I think there wasn’t a very satisfying answer for why that was the case. But then there was a more recent paper in the 1900s, more recently to us, Landauer came up with the argument that you’d have to spend energy to delete information, and Maxwell’s demon would basically have access to so much information by doing this thing he’s doing that he’d have to delete some. And for. When he’s deleting information, he’d have to spend energy, which would then make sure entropy increases. So even though we’re not at the level where we can be Maxwell’s demon, I think your, question is very visionary towards a future where we store so much data we have to delete some.

Swyx [00:28:17]: Yeah, no, I wasn’t, I wasn’t obviously going to that, to that extent. It does remind me of air conditioning. That’s kind of how air conditioning works. But also, I think it’s just a question of why don’t you just buy every sensor in the world and just over. Ridiculously over-instrument, over-replicate everything?

Liam Fedus [00:28:33]: Oh, we do that.

Swyx [00:28:34]: Right? Yeah, okay, yeah, okay. There we go.

Liam Fedus [00:28:35]: Yes, yeah.

Swyx [00:28:35]: Okay, it works out. Right. All right.

Liam Fedus [00:28:37]: Yeah, I think, I think it’s a matter of

Swyx [00:28:39]: Confirmed.

Liam Fedus [00:28:39]: Yeah, exactly. Are you getting visibility to individual atoms, which is infeasible? But no, we add a huge amount of telemetry, and that basically reduces the number of hidden variables, if you will.

Swyx [00:28:49]: Yeah. Yeah. And just out of curiosity, you guys do you worry that you’re just in California? Don’t you want to do this in Nepal and Australia?

Ekin Doğuş Çubuk [00:28:59]: Yeah, we could. We have plans to open more labs.

Swyx [00:29:02]: Because obviously gravity and where you face and where you. Where you are matters.

Ekin Doğuş Çubuk [00:29:07]: Also expertise can change, right? Here we have certain kinds of expertise. For example, Stanford Physics Department, Engineering Department has certain kinds of expertise that really helps us because we can hire from there. Our researchers can collaborate. As you said geography can matter, but then different cities can have some differences of expertise within physics, chemistry.

Swyx [00:29:27]: Yeah. Oh, we have Zoom

Ekin Doğuş Çubuk [00:29:28]: Yeah.

Swyx [00:29:28]: For that.

Ekin Doğuş Çubuk [00:29:29]: Yeah. But still

Swyx [00:29:31]: It’s not like your experiments are going to be affected by where you are.

Liam Fedus [00:29:34]: Yeah. Yeah, the future of Periodic is not a single lab.

Swyx [00:29:38]: Yeah. You gotta head up your private jet and go to space and you know.

Liam Fedus [00:29:42]: Right. Yeah, I think it’s necessary to have this sort of network of labs. And like, yeah, I think as Doğuş was just kind of pointing out there’s an optimal place for each of these based on expertise or materials constraint. There’s many different constraints, permitting.

Connecting Simulations With Physical Experiments

Brandon [00:29:56]: Okay, so one of them is. So one thing I-I’ve been curious about is how do you deal with computational tools? Or how do you integrate computational tools into a decision-making workflow, especially when, let’s say, computation and experiment don’t necessarily, meet? It’s also theory in terms of high-level understanding. And yeah, how does that sort of work together?

Ekin Doğuş Çubuk [00:30:22]: Yeah. So, one thing that’s really helpful here is there are certain things that are easier to do in simulation and more accurate in simulation. There are certain things that are easier to do in experiment and more accurate in experiment. In general, of course, experiments are more accurate, but certain measurements are very hard to make with experiment. So you make a bad version of it, so you get inaccurate data. Let me just give you some examples. So one of them is, we use density functional theory to estimate formation enthalpy of materials and

Brandon [00:30:51]: Sorry, quick pause. Can you explain densital density functional theory and formation entropy?

Density Functional Theory and Formation Enthalpy

Ekin Doğuş Çubuk [00:30:55]: Yeah, of course.

Brandon [00:30:56]: Sorry, enthalpy, yeah.

Ekin Doğuş Çubuk [00:30:57]: Yeah. So, density functional theory is probably the most commonly used simulation method for materials. It kind of comes from Hohenberg–Kohn theorem, and Kohn got a Nobel Prize for this. He. When he realized that, sure, quantum mechanics is very expensive because you basically have this exponential Hilbert space for every wave function, so you have to solve this very difficult problem, especially as the system size grows. Kohn and Hohenberg realized that turns out you don’t need to do it at exponential space. All you have to do is think about the charge density, at least for the ground state of quantum mechanical systems. So this it’s actually the theorem is pretty simple. So even I think an undergraduate in physics can understand. But the idea is all the properties of a quantum mechanical system ground state is a functional of the charge density, which is amazing because charge density is just this three-dimensional object, whereas the Hilbert space and wave functions are exponential. So that was a really interesting observation. And then Kohn published another paper, I think with his postdoc, Sham, Kohn–Sham, wave functions, kind of had a practical way to solving some approximation of quantum mechanical properties of materials. And today, we use it very much, both in our lab and externally. And then formation enthalpy is one of the measurements we can make to understand the energy of the material. Basically, we want to ask, what is the energy that this material has? Because if that energy is higher than other materials these atoms can come into, then it’s unlikely that this material will be able to be made. So stable just means like it’s on the convex hull of other materials, and their energies. Was that clear enough, or should we.

Brandon [00:32:42]: Maybe a simple high-level point. So density functional theory is a way of taking something which is exponentially hard, in number of electrons

Ekin Doğuş Çubuk [00:32:51]: Right

Brandon [00:32:52]: Or number, in your system size, and then reducing it approximately with some introduced error to maybe usually an n-cubed approximation. So now you can compute quantities which are, you know. You can now compute things which are just uncomputable, but at some cost, right? If you could do it perfectly, you would — we wouldn’t need a lab. But we can’t. This might be a good callback to our, actually our second episode ever. Heather Kulik had said, she famously said, “There is no AlphaFold for materials, not just computationally, but the ground truth just doesn’t exist.” Like, we don’t really know what material crystal structures look like, and DFT is generally our best route towards.

Ekin Doğuş Çubuk [00:33:34]: Yeah, I think so. Maybe two things I would add is DFT by definition doesn’t have to be an approximation. The Hohenberg–Kohn theorem shows it

Brandon [00:33:42]: It doesn’t

Ekin Doğuş Çubuk [00:33:42]: It can be exact. But I think, as you’re pointing out, the exchange correlation functional we don’t yet have access to. And then for the charge density-based models, we don’t know what the kinetic energy functional is. And then even if we had perfect DFT, I still think we’d need a lab because we. Even if we had perfect DFT, we cannot fit ten to the twenty-three atoms in a computer.

Brandon [00:34:03]: Yeah.

Swyx [00:34:04]: You’re very obsessed with that number.

Brandon [00:34:06]: But. No. Well, okay. Yeah. But with exponentially growing compute, you can argue that in n-cubed scaling size, eventually you could, in principle, calculate things. You just wait till the pause long enough.

Ekin Doğuş Çubuk [00:34:17]: Oh, yeah. We need massive computers, yeah.

Brandon [00:34:19]: But yeah. But as long

Ekin Doğuş Çubuk [00:34:20]: It’ll take a while.

Brandon [00:34:20]: Yeah. But as long as n-cubed is a good scaling, you can, you can.

Swyx [00:34:24]: As the non-scientist, I will make an observation, which I don’t know if you want to throw around or anything like that. So I came from finance. We had the Gaussian copula, which was a way of pricing credit default swaps, by doing correlations and reducing everything into a single kernel. And it’s very. It feels also spiritually similar to the VAE, in terms of like, condensing all these things into a single sort of parameter. I wonder if just there’s the same trick everywhere. Yours sounds, a bit more high-dimensional than mine, which VAE is just like, one sigma thing. But sounds the same?

Liam Fedus [00:35:01]: Yeah, the charge density file is still quite a big file.

Swyx [00:35:04]: Yeah. Right. You hand wave a bit, but it’s good enough. This is, this is the highest order bit. Yeah.

Ekin Doğuş Çubuk [00:35:10]: I personally don’t know of a better method to predict the stability of a new material. It’s definitely not perfect, but it’s definitely better than other methods I can think of. And yeah, so going back to your question, the reason we use them is for certain things it’s easier to simulate than experiment, and they kind of complement each other well. And then we can do this in a loop. We feel like simulations will never be enough by themselves, but in the loop of simulations, AI, and experiments, I think we can make progress much faster than before.

When Simulations Fail: Microstructure and Calibration

Brandon [00:35:41]: One thing that simulations let you do is scale up much more quickly. Because now compute is, much easier to bring online

Ekin Doğuş Çubuk [00:35:48]: Yeah

Brandon [00:35:48]: Than bringing in bringing on new labs. How do you avoid. Or let’s say you scale up a bunch of DFT, how do you avoid in having your models over-index on that and still be grounded that the real world is ultimately the truth you care about? If you. You know, how do you sort of balance that?

Ekin Doğuş Çubuk [00:36:05]: Well, we scale up the lab as well.

Brandon [00:36:06]: Yeah, you scale up the lab.

Ekin Doğuş Çubuk [00:36:07]: Right, yes.

Brandon [00:36:07]: But you could. I could imagine scaling up DFT to millions, hundreds of millions given modern compute, whereas your lab, I would imagine, your hundreds, thousands a day? I don’t know. Something still fairly. Well, even in a really high-throughput case you’re still fairly limited by comparison, right?

Ekin Doğuş Çubuk [00:36:27]: Yeah. One thing is humans, but also LLMs, are kind of pretty aware of the limitations of DFT. For example, even if we can do, as you said, hundred million trials with DFT

Brandon [00:36:39]: Yeah