The future of ultrafast materials and devices
Engineer Aaron Lindenberg is an expert in the ways atoms and electrons move through materials.
He uses X-ray “flash photography” to make movies of atoms moving at ultrafast speeds to predict the fundamental limits of electronics in future consumer devices, solar cells, and AI chips. He estimates we are “many orders of magnitude away” from the physical limits of both speed and energy efficiency in our electronics. Today’s computers are at least a thousand times slower than they could be, Lindenberg tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. Since we started this show eight years ago, it's become an archive of amazing and impactful work by my Stanford colleagues. Research is not something that just happens in the lab, and as you'll hear on this show, the research at Stanford can impact areas like health, technology, law, and business, and many other topics that can affect everyday life. We hope you'll tune in to learn more about how research has the potential to help your life and to help the lives of people you care about in your family and your community.
[00:00:32] Aaron Lindenberg: At its basis, we're interested as a material scientist in understanding how materials work and understanding where the atoms are, where the electrons are, and then at this microscopic level, how this amazing complexity leads to functionality. So if you want to make a device out of a material, you have to understand how electrons and ions flow through these structures. and if you can do this, then you can start to kind of harness these kind of fundamental processes and make devices like solar cells or batteries, things like that.
[00:01:03] Russ Altman: Right.
[00:01:09] This is Stanford Engineering's The Future of Everything, and I'm your host Russ Altman. If you're enjoying this show, please share it with somebody you care about. Personal recommendations are one of the best ways to spread news about the show.
[00:01:21] Today, Aaron Lindenberg will tell us that there's a fundamental trade-off in atomic processes between their speed, the energy it takes to make them go, and the reliability of their output. It's the future of ultra-fast materials.
[00:01:36] Before we get started, a reminder that we have the Future in a Minute at the end of this conversation, I'll ask Aaron some quick questions, he'll give me some quick answers, it'll be the Future in a Minute. Before we get started, another reminder to please share the show with somebody you care about who would benefit from knowing about The Future of Everything
[00:02:00] Well, materials are literally what makes the world around us. In particular, electronics, optics, batteries, power, these are key materials that fuel our technology that we all have become totally dependent on. A key idea is that these materials are often what we call non-equilibrium or dynamic. That is to say, at the atomic level, these materials change in response to processes.
[00:02:27] So for example, a proton, a, a ray of light, hits a material in a solar cell, and it turns that dynamically into electricity. That's distinguished from equilibrium materials, where they're resistant to change, and actually they don't change when you give them inputs, and sometimes that's a positive as well. But for most of electronics, we're looking at non-equilibrium or dynamic materials, and our ability to understand these materials has become revolutionized by our ability to measure them and look at them on ultra-fast timescales.
[00:03:01] Well, Aaron Lindenberg is a professor of material science and photon science at Stanford University, and he studies materials at the highest speeds and in the smallest scales in order to understand how they work. Aaron will tell us how these measurement technologies are enabling new understanding that promises to increase our ability to create better and better materials.
[00:03:26] So Aaron, to start out, how did you decide to work in this area of material science, which we'll get to?
[00:03:32] Aaron Lindenberg: Yeah. Actually my background is in, is in physics. and actually, you know, one of, sometimes you learn, you have experiences where, you know, they kind of imprint on your brain, and they kind of change how you think about things.
[00:03:47] So for me, actually in grad school, I remember really clearly actually learning about a problem in biology, actually, so this is not material science. But this was kind of what got me on this path, and it was a problem about how, hemoglobin and myoglobin work and how they carry oxygen through the human body. And so this was a problem where people used these amazing techniques called X-ray diffraction to kind of make images of where the atoms were in this kind of amazingly complicated crystal. And when, so Max Perutz was the guy who kind of did this and won the Nobel Prize back in the 1960s, I think, for this. And when you read this-
[00:04:27] Russ Altman: I met Max, I met Max before he died as an undergraduate, and it was a huge- Yeah ... thrill. Yeah.
[00:04:32] Aaron Lindenberg: So the, the story, at least as I learned it, was that when he made this picture of this kind of molecule, it was so complicated and there were, the structure was so unbelievably complex that there was no way that the oxygen atom could kind of migrate through this complex structure and bind to this central heme group.
[00:04:51] And so they, on the one hand, they had this idea that if you could understand the structure of something, then you could, this would allow you to understand how it functions and all kinds of things like that. and rather in this case, it created a mystery. And what people eventually realized was that this molecule was dynamic, it was opening up, in this amazingly complex way to allow the oxygen to come in. So that was kind of how I got really excited, not just about material science, but also about kind of the dynamics of, of how materials move at the atomic scale.
[00:05:22] Russ Altman: Great. That's a great, that's a great origin story, and I, and as somebody who studies biology, it's, it's great to know that we can spawn, great physics, great material sciences work.
[00:05:34] So for people who don't think about material science all the time, can you just give us like a little thumbnail sketch of what is the goals of, of material science, and particularly from your perspective and, and, and the problems that you're working on, kind of getting us to what are the big questions that like motivate your lab?
[00:05:50] Aaron Lindenberg: Yeah. it's, it's a, I would say it's, it's a question, there's a lot of fundamental science that goes into it, just asking really fundamental questions. And then there, and then also there are a lot of kind of really technologically relevant applied questions that come into it. You know, at its basis, we're interested as a material scientist in, in understanding how materials work and in understanding where the atoms are, where the electrons are. And then, at this microscopic level, how this, how these, this amazing complexity leads to functionality.
[00:06:25] So, if you want to make a device out of these thing, out of a material, right, you have to understand how electrons and ions flow through these structures. And if you can do this, then you can start to kind of harness these kind of fundamental processes and, you know, make devices like solar cells or batteries, you know-
[00:06:42] Russ Altman: Right
[00:06:42] Aaron Lindenberg: Things like that.
[00:06:44] Russ Altman: So, so, that's great, and I know that part of what's exciting in your field right now is kind of unbelievable ability to measure and see atoms, like at the atomic level. You describe this, challenge of understanding hemoglobin, and it sounds like the, technology has really advanced. And so, can you paint a picture for like the kind of power tools that you now have at, have at your disposal that enable super detailed understanding of what's going on in, in material?
[00:07:13] Aaron Lindenberg: Yeah. Right. A, a lot of it actually in some sense connects to this original hemoglobin problem, right? So this was, this was, the way that Max made these measurements in the end, Max Perutz made these measurements, was by using, X-ray scattering approaches.
[00:07:29] So they took an X-ray beam, which is essentially a beam of, of light with very, very short wavelength, wavelength comparable to the spacing between atoms, and essentially bounced it off of a crystal of these things. And by measuring the diffraction pattern, which is essentially an interference effect, the, the beams of light kind of scatter off of each of the individual atoms, and create interference effects in the same way that you see interference effects when you, you know, look at, light, you know, reflecting off of a puddle on the, on the ground or when you look at light reflecting off of a, a DVD or something like that. So essentially, by making these types of measurements, you could kind of work backwards and reconstruct where the individual atoms were in the, in the structure.
[00:08:16] And then more recently, and this is where I kind of started out, as a grad student, really exciting new tools have been developed. Some of them just up, up the road from, from Stanford at the SLAC National Accelerator Lab. And, and these are tools which allow one to do this type of experiment. It's, it, it, but, but not just measure the structure, but measure the dynamics of this process, how it's changing on amazingly fast timescales.
[00:08:43] And sometimes people have the sense that, you know, these very fast timescales are things that are, you know, maybe not that important to our everyday lives, but, but really materials, when you zoom in, on them, the atoms are moving on amazingly fast timescales, femtosecond timescales.
[00:08:59] Russ Altman: Yes. So this is great. So give us a sense, 'cause I, I know, I know a little bit about this, and it's, it gets hard to think about things being so fast. So can you lay the landscape for somebody who doesn't think about this all the time, about exactly how fast we're doing it, maybe in comparison to other phenomenon that they might be roughly familiar with?
[00:09:16] Aaron Lindenberg: Yeah. Yeah. One, one way it's oftentimes described is that, so I, I mentioned one femtosecond, so that's 10 to the minus 15 seconds. And so, you know, one way to think about it is that one femtosecond is to one second, as one second is to something like the age of the universe.
[00:09:32] Russ Altman: Okay.
[00:09:33] Aaron Lindenberg: So, that's just one way to kind of, you know, appreciate it, how short this timescale is. It's really beyond the human mind to kind of comprehend it in some sense. But nonetheless, materials, are moving on, on these types of, these types of timescales. And so, it's a challenge, you know, for the, you know, in the same way that if you take a picture, of something, you know, and the object is moving very, very, very fast, it will be blurred out, and you'll get a blurry image. You know, people developed flash photography as a way to-
[00:10:06] Russ Altman: Right
[00:10:07] Aaron Lindenberg: ... kind of get around this, right? And so this kind of illuminates the object for a very short amount of time and allows you to take a snapshot of this process. That's kind of how, what we're doing in, in a lot of these experiments, we're kind of taking snapshots of these structures, as they evolve on, on these very short timescales.
[00:10:25] Russ Altman: So on these,... so let's talk about the snapshots and the actual experiments that you do. So you get a piece of material that you find interesting, I guess, right? Is this a tabletop measurement device, or is this a huge device? And, and how do you ,and your, and your students, and your ,and your other research collaborators, how do these experiments actually get done?
[00:10:45] Aaron Lindenberg: Yeah. There's a lot of different-
[00:10:45] Russ Altman: And what are the readouts that you then analyze?
[00:10:47] Aaron Lindenberg: There's a lot of different kind of approaches to this. Some of them are really tabletop type experiments where you can, you know, a single student can, can really get to work and, and in a day, you know, make a snapshot of, of, of these types of things.
[00:11:02] You know, when, when you start to push the length scales, when you start to really want to zoom in at the atomic scale, we tend to often make use of the larger scale facilities. So I mentioned the SLAC National Accelerator Laboratory. This is a multiple kilometer linear accelerator, that, you know, was originally built for kind of particle physics type experiments and has been kind of repurposed over the last few decades. And now it's essentially a source that allows one to create bursts of X-rays to do exactly these types of, this type of flash photography on a battery of particles.
[00:11:38] Russ Altman: And if I understand, you're not just taking so-called still photographs, you're also making movies. So, tell us how we make the movies, but more importantly, like what can we learn from a movie that you might not get from a still photo?
[00:11:51] Aaron Lindenberg: Yep. Yeah, yeah, so, so the way... essentially you make a movie in the same way that when we, you know, you make a movie in a, you know, that, you know, that you watch at the movie theater, right? Essentially it's a whole bunch of still photos put together in a way that's sequenced such that when you kind of run them from one to the next, you, you turn this into a, a real movie that captures-
[00:12:14] Russ Altman: Yes
[00:12:14] Aaron Lindenberg: Some dynamical process.
[00:12:15] Russ Altman: And who are the actors in these movies? Are you looking at like at an individual carbon or oxygen molecule? Are you... Atom? Or are you looking at the electrons? I mean, how, how fine a grain are these photos?
[00:12:26] Aaron Lindenberg: Yeah.
[00:12:27] Russ Altman: Excuse the extended analogy.
[00:12:29] Aaron Lindenberg: Sometimes we're looking at, this, this might be a crystal, like in the, in the example of hemoglobin, this were, this was many, many copies, you know, something like 10 to the 23 copies of these kind of individual molecules all kind of locked, you know, together to form an overall kind of periodic crystal that, you know, you could see with your own eyes.
[00:12:50] But more and more people are kind of pushing to do these measurements at the nanoscale, where you might imagine, you know, for example, a single nanocrystal, a single quantum dot, in the focus of this, of this beam, and, essentially taking a snapshot at the nanoscale of, of this evolving object.
[00:13:08] Russ Altman: Great. Great. Okay, so we have a, this gives us a, I think a really good, it took some time, but it's important to have this groundwork on what your capabilities are. And so, I guess the next question is what are the kinds of questions you ask? I know just... I'm intrigued because you've had, you've made statements about we're looking at the speed, the fundamental speed limits for some kind of electro- electronic or optical devices. That sounds important to me. So maybe let's start, tell me about speed limits.
[00:13:34] Aaron Lindenberg: You're right. Yeah, this is something that has motivated us for a long time and it's actually, again, one of these problems that is really interesting from a fundamental perspective and, and also, as you mentioned, has a lot of really important kind of problems in, in, in technology, right?
[00:13:52] So, you know, from a fundamental perspective, you know, this comes down to questions like, like how fast can you switch a material? How fast can you, you know, we oftentimes in code a zero or a one in a device-
[00:14:05] Russ Altman: Right, right
[00:14:06] Aaron Lindenberg: ... by the position of an atom within the unit cell. And so you could ask questions like, how fast can you really switch that structure, right? This defines, in the end speed limits that, you know, define how fast a computer can, can operate or how fast a device can, can function. And it turns out there's been a lot of kind of really interesting and, and amazing kind of theoretical developments as well, over the last couple of years. Essentially people trying to take ideas that you know, in high school and kind of early in, in college, you learn thermodynamics, which is the, you know, kind of a, a way of thinking about processes at equilibrium.
[00:14:46] Yes ... and so it's only very recently that people have been able to kind of extend those ideas to non-equilibrium processes where there's dissipation, where energy is, is lost in, in these processes. And so these types of, of snapshot experiments that we, apply, can let us get a handle on these things, visualize these processes.
[00:15:06] And then the, the, the next important step, and this is again where we're really excited, is kind of thinking about controlling these processes, right? So if you can see these processes in interesting ways, then you can start to think about, well, you know, how can I engineer this process? Is there a way for me to, you know, control how I switch a material from a zero to a one state in a way that minimizes the energy cost or maximizes the speed by which it occurs?
[00:15:32] Russ Altman: So I don't know if this is a fair question, but with respect to speed limits of things like computers, are, are we at a position where you can tell me that we are pushing up against it and that the last 40, 50 years of speed-ups that you and I have experienced just as, as a, as a human in, in, in our electronic devices, is that era coming to an end? Or do you see plenty of headway so that, yeah, of course there are physical limits, but we, we have not gotten anywhere close to them.
[00:15:58] Aaron Lindenberg: Yeah.
[00:15:58] Russ Altman: What's the general sense?
[00:16:00] Aaron Lindenberg: It's, it's quite interesting. We're, we're, we're many, many orders of magnitude away in both speed, in both the speed limits and in the energy costs.
[00:16:10] Russ Altman: Okay
[00:16:10] Aaron Lindenberg: Right? So, you know, a typical, you know, a typical computer, you know, might operate at gigahertz, like, like frequencies, right? That's 10 to the 9, kind of operations per second, right? And there are processes like I, that I was talking about, you know, essentially that are related to kind of really fundamental processes and how you encode information in a information storage device where you could push this to picoseconds, 10 to the minus 12 instead of 10, you know, instead of 10 to the minus 9.
[00:16:40] Russ Altman: So that's 1,000 times faster right there.
[00:16:42] Aaron Lindenberg: 1,000 times faster, yeah. And then energy I mentioned is also, you know, in some sense even more important.
[00:16:47] Russ Altman: Yes.
[00:16:47] Aaron Lindenberg: Right? You know, you know, in this age of AI and, and so on, right? We all know that there-
[00:16:52] Russ Altman: Server farms.
[00:16:52] Aaron Lindenberg: Yes ... server farms, right, are, you know, are equivalent of, of a nuclear reactor in, you know, in terms of energy costs, right? And so, you can ask the, again, this question of like, you know, how much energy do you really need to like make a single operation, like a, a single transistor? Imagine like zooming into your computer-
[00:17:09] Russ Altman: Yeah
[00:17:09] Aaron Lindenberg: Right, and saying, "Well, how much energy do I really need to kind of switch that transistor," right? And, and it turns out, again, in this case, we are many, many orders of magnitude away from the kind of fundamental limits. It turns out people actually in at least for an equilibrium process, kind of understand pretty well what that kind of fundamental limit is. It's something, something called the Landauer limit, and it, and it essentially, it kind of encodes the kind of fundamental amount of energy you need to kind of erase, or a, a, a one or, you know-
[00:17:43] Russ Altman: Yeah
[00:17:43] Aaron Lindenberg: ... Take a one and transition it into a zero. And it turns out that the energy costs that, that we're at, you can put some numbers on these things actually, right? You know, people are pushing a, a lot talking about something called at- the attojoule front- frontier, which is where you're trying to
[00:17:58] Russ Altman: move devices. Yes. Atto is A-T-T-O, right? A-T-T-O and
[00:18:01] Aaron Lindenberg: it's-
[00:18:01] Russ Altman: A-T-T-O ... it's even smaller than pico
[00:18:04] Aaron Lindenberg: Even smaller than pico, even smaller than femto, right? This is-
[00:18:06] Russ Altman: Yeah
[00:18:07] Aaron Lindenberg: ... this is 10 to the minus 18 joules. That's one attojoule, right? Right. and so, you know, people are, are, there's a lot of excitement about making devices that operate with energy costs associated with that type of, that type of, of, of dissipation.
[00:18:21] Russ Altman: Yes
[00:18:22] Aaron Lindenberg: ... And it turns out that the fundamental limits defined by the Landauer limit are something like 10 to the minus 21 joules, right?
[00:18:28] Russ Altman: Okay.
[00:18:28] Aaron Lindenberg: So this is a zeptojoule, actually. There's a word for even that.
[00:18:31] Russ Altman: Wow, okay. So if I'm, if I'm, if I'm hearing you correctly, this is super good news because it means that, these server farms, they, they might not... I mean, it's not tomorrow. I understand that this is basic discovery, and there's always time to translate it into devices and capabilities for engineers, but we have plenty of headroom.
[00:18:50] Again, just as you were talking about speed, we also have energy efficiency opportunities that make sure, that indicate we should not give up on very, very low energy versions of the kinds of things that right now we're we, we have big batteries or big server farms, that there's a lot of, potential.
[00:19:10] Aaron Lindenberg: That's right. And, and actually one other interesting point, it turns out these things are int- interconnected, these ideas of speed and energy costs-
[00:19:16] Russ Altman: Yeah
[00:19:17] Aaron Lindenberg: ... Are really kind of interlinked, linked in really interesting ways. Like, one of the reasons why we're kind of, in terms of these energy costs, not at these kind of fundamental limits, it turns out, is because computers are intrinsically dynamical things, like we're talking about. They're non-equilibrium processes. You know, computers are not quasi-static in the way that we kind of think about-
[00:19:39] Russ Altman: Right
[00:19:39] Aaron Lindenberg: ... Thermodynamics as kind of applied on an equilibrium process. And so when you try to make processes happen Dynamically over very, very fast timescales, it turns out you need to put more and more energy into them to drive.
[00:19:53] And the faster you wanna go, the more energy you need to dissipate. And so there's an intrinsic kind of trade-off between these things that is really interesting.
[00:20:01] Russ Altman: I see. So should we think of it as a speed/energy trade-off? Is that basically the trade-off that's happening?
[00:20:07] Aaron Lindenberg: Exactly right. People some- you sometimes call it, there's a, it, there's a kind of a, a speed kind of time uncertainty principle-
[00:20:14] Russ Altman: Okay
[00:20:14] Aaron Lindenberg: ... in some sense that kind of encodes-
[00:20:15] Russ Altman: Okay. Yeah. So that's good though in a, because once that trade-off, once, once we have it all under control, there are things that we don't need to be fast, but we need to be efficient, and there are other things, like our, you know, like our phone calls or whatever, that we need to be fast, but perhaps we would be willing to pay a price in efficiency.
[00:20:34] Aaron Lindenberg: Exactly. That's right. And then another thing that comes up often is reliability, right? You know, when you store information, you, you want that-
[00:20:41] Russ Altman: Good, good, good. Yes
[00:20:41] Aaron Lindenberg: You store one or something, you want that to be stable. So when you, when you, you know, save a photo of your kids and you wanna go back and look at it 10 years from now, it better be there, right?
[00:20:50] Russ Altman: It would be nice.
[00:20:51] Aaron Lindenberg: Right? And so, so, so this aspect of reliability also is in, is encoded in this kind of trade-off. You can make things less reliable, but then maybe switch and operate more effect- more fast. Or you can make them more reliable, but maybe then there are more energy costs associated with them.
[00:21:10] Russ Altman: This is The Future of Everything with Russ Altman. We'll have more with Aaron Lindenberg next.
[00:21:29] Welcome back to The Future of Everything. I'm Russ Altman, and I'm speaking with Aaron Lindenberg from Stanford University. In the first segment, we got a little tutorial on our ability to measure the atomic properties of materials with amazing technologies that give us the scale in both space and time that makes very fast processes look slow because the measurements are so fast.
[00:21:53] In this segment, I'm gonna ask Aaron about simple things like solar cells, randomness, and how AI is helping him in his work Don't forget, at the end of the segment, we'll have the Future in a Minute, where I'll ask some quick questions and get some quick answers.
[00:22:09] So Aaron, in this section, I wanted to start out with things that people are pretty familiar with, batteries, solar cells. What are they to you as you look at them as a material science, and what are the opportunities kind of to make, to understand them better and then to make them better?
[00:22:24] Aaron Lindenberg: Right. Yeah, so like, I mean, think about, let's take the solar cell as an, as an example, right? So this is a device which takes photons, light from the sun, and turns it into electricity, right, at a, at a fundamental level, right?
[00:22:37] And, but if you zoom in, if you, if you think about, you know, what's really happening at the- atomic scale, you know, and, and imagine that you have, you know, perfect resolution to see these processes as they unfold, right? Then what you actually see is something where, you know, maybe a single photon of light, is absorbed in the material, silicon or, or whatever that material is, right? It creates an excited electron, that electron starts to move through the material, it's scattering and bouncing off of, of atoms within the crystal structure.
[00:23:10] Russ Altman: And, and excited means it has a lot of energy, right?
[00:23:13] Aaron Lindenberg: Yeah.
[00:23:13] Russ Altman: It, like it has absorbed all that energy from what, the light, and now it is, it is not gonna just sit there quietly.
[00:23:20] Aaron Lindenberg: That's right. Yeah, and so in the end, of course, if you want this device to work, you have to take that energy, right, that energy, you've converted that energy from light into electronic, right? But now it has to make its way out, and there has to be a current or a voltage that is developed, right, right, you know, across two leads to actually power some, some device, right?
[00:23:40] And so, so it turns out that if you want to understand the ultimate efficiencies of this process, right, like how efficient can you make a solar cell, you know, can you, can, well, it, it, the processes that, that come in here, the timescales and the length scales are amazingly short in time and amazingly, small in, in length scale, right?
[00:24:02] So on picosecond timescales, this electronic energy, some of that energy is lost as the kind of, this kind of hot electron kind of relaxes.
[00:24:12] Russ Altman: Right.
[00:24:13] Aaron Lindenberg: if you want to kind of capture that energy, then, then you need to find a way to make that device, you need to find a way to extract that energy on these very short timescales.
[00:24:22] Russ Altman: Yeah, before it basically just turns into heat.
[00:24:24] Aaron Lindenberg: Exactly. And once, once it's turned into heat, now suddenly you, this is a, a, a form of energy that is hard to use for performing work in that-
[00:24:34] Russ Altman: Right
[00:24:34] Aaron Lindenberg: Right, from a thermodynamics perspective. So, so it, so in the end, understanding, being able to see these processes allows one to, number one, you know, think about new, find new materials, work in kind of feedback loops where you, you know, can kind of think about a whole bunch of different material systems where you've tuned the properties of the system in different ways. And, kind of run these kind of operando-like experiments where you kind of see the device as it operates. You measure these kind of processes at the atomic scale, and you measure maybe simultaneously the operation of the solar cell, how much current is actually flowing through the device.
[00:25:15] And you can kind of start to correlate these things and, and use this as kind of a design principle to kind of discover new materials to, to optimize the performance of the material and so on.
[00:25:28] Russ Altman: Yes. it, it, it, it, it does remind me of your very first founding example where the oxygen was trying to get through that protein, and now you've drawn a very similar picture in my head where there's this electron and you're trying to kind of channel it into, with all its other fellow electrons, basically into a stream of electrons that are heading towards presumably some kind of positive charge to create the current.
[00:25:52] And, and, and I can imagine that different materials would be either better or worse at kind of allowing you to channel those electrons. So you've written about randomness. How does that so, this whole thing seems kind of random, but I think you mean random in a, in a much more kind of theoretically manageable way. So tell me-
[00:26:11] Aaron Lindenberg: Yeah
[00:26:11] Russ Altman: ... about the role of the randomness in your work.
[00:26:13] Aaron Lindenberg: Right. So actually, you know, even, you know, taking that, that same example we just talked about, the solar cell, when that, when that photon gets absorbed in the material and creates electronic excitations, you also, it turns out, drive reconstruction of the lattice. The atoms move around in complicated ways.
[00:26:34] Russ Altman: Ah.
[00:26:34] Aaron Lindenberg: So this is, again, some materials, this is kind of a really important process, and people think even that this dynamical motion of the atoms protects and, you know-
[00:26:44] Russ Altman: Uh-huh
[00:26:44] Aaron Lindenberg: ... makes the, the material more efficient in, in interesting ways. And so this is a, this is a process that involves kind of, the way, the way we think about it in, in material science is it's overcoming an energy barrier. So there's some, there's some kind of energy. It's, it's just like when you're kind of climbing a mountain, right? And there's a, a mountain in front of you.
[00:27:04] Russ Altman: Right.
[00:27:05] Aaron Lindenberg: You kind of have to kinda, you know, try to make your way up, and it takes a lot of work, of course, to kind of overcome that barrier. In the same way, at the atomic scale, there are energy costs that are associated with overcoming barriers and switching materials, in that way. And, and this process, it turns out, is, has a really interesting element of randomness. Sometimes we use the word stochasticity to-
[00:27:27] Russ Altman: Yep ...
[00:27:27] Aaron Lindenberg: to describe this. and, and the way we think about this is that the, the material is kind of, you, you know, you're maybe trying to take energy from the sun, and kind of push this material over this barrier, right? But it's kind of fluctuating and, and, it's, you know, making attempts trying to get over the barrier, but falling back and, and so it turns out that at the atomic scale, at the nano scale, these types of fluctuations, become really, really important, right? If in, in the same way that, that if you zoom in on a, a material,
[00:28:01] Russ Altman: Right, right
[00:28:01] Aaron Lindenberg: ... at room temperature, right? If you could if you could actually see what the atoms are doing, it's, they're not just sitting there steady. They're fluctuating and vibrating in really kind of complex random ways. And so that randomness and understanding how that, how that, that, that, how that plays a role in the efficiency is a, is a really important process.
[00:28:20] And it's, it's a really challenging problem as well because when we do these, when we make these movies like I was describing, right, where we kind of make snapshots of, of many of these processes and put them together, oftentimes we, we hope to, we kind of do these measurements in, in kind of an averaging mode where you might, like, run the experiment many, many times-
[00:28:40] Russ Altman: Right, right
[00:28:40] Aaron Lindenberg: ... and take many different snapshots and put them together. And the assumption that we make when we do these experiments, when we do those experiments, is that kind of the process is kind of always following the same trajectory every time.
[00:28:52] Russ Altman: Yes. Yes.
[00:28:52] Aaron Lindenberg: And this turns out to be a bad approximation when you zoom in at the nanoscale. These materials are fluctuating, and each time you kind of run a chemical reaction, it might do something different each time. And so, how to kind of capture that and deal with that is a really central challenge.
[00:29:07] Russ Altman: Yeah, I find that to be very intuitive, 'cause you see these pictures that people take of rivers where they leave the camera on for a long time, and the river looks like this beautifully smooth, like, flow of water.
[00:29:18] But anybody who's actually watched a river in detail sees that there's all of these eddies and there's all these other motions. And, and what I'm hearing you say is you need to capture those. And, and now we can see why we're going back to these ultra-fast movies and pictures that you're able to take, 'cause I'm, I'm gathering that it actually is within range to get these kinds of photos, so to speak-
[00:29:39] Aaron Lindenberg: Yeah
[00:29:39] Russ Altman: To understand that randomness and how you can kind of harness it, or at least distinguish between materials whose randomness seems to be, more favorable for the application at hand, versus ones that are actually fighting it too much.
[00:29:52] Aaron Lindenberg: Right. The river example's a really good one, actually, 'cause it also gives you, it, it brings us to this ideas, we often use the word heterogeneity. So materials are, are heterogeneous in the sense that if you look at different parts of the material, different parts look different, right?
[00:30:08] Russ Altman: Yep.
[00:30:08] Aaron Lindenberg: There's defects, there, there are all kinds of kind of complexities, and you can't kind of just assume that everything is doing the same thing and everything, you know, when you make these kinds of averages, you lose information about the dynamics in the same way that a river has this amazing complexity.
[00:30:26] Russ Altman: So in the, in the last couple of minutes, I just wanted to ask you about AI. I also know from, from your work that your, your, your group does use AI, and I'm wondering, like, what has the impact been? Is it, is it a a, a significant impact, or is it somewhat, peripheral to, to the work? And what are the opportunities? Are, are you bullish?
[00:30:45] Aaron Lindenberg: Yeah, it's, it's a, I think it's a really exciting time and, and there's no question that, that there are ways in which, you know, AI is gonna play a really central role. You know, we-- I talked about these diffraction experiments, for example, where you bounce a beam of X-rays off of material, right? This creates an amazingly complex pattern on a detector that we measure.
[00:31:06] Russ Altman: Right.
[00:31:06] Aaron Lindenberg: Right? And, and these, these images are, are kind of coming in at very high rates. It's, it's a massive data problem just to kind of analyze these images, right? And so people are thinking a lot about, you know, how to use AI to kind of, provide kind of real time feedback on an experiment, right?
[00:31:26] Where you, you know, you might, as you're running the experiment, get some, some feedback which allows you to change some parameter, you know, as you kind of develop a new material or as you kind of build a new device. so there's a lot of excitement there.
[00:31:40] Another, another interesting example is in this idea of, of control of a, of a process that I talked about previously.
[00:31:49] Russ Altman: Yes.
[00:31:49] Aaron Lindenberg: But we, we're thinking a lot about ways in which we can, for example, we're trying to measure some device. As it operates, we apply some voltage waveform, some voltage switch that switches the material, right? And you can think of lots of different kind of waveforms, and the question is what is the optimal one that minimizes the energy cost like we were talking about before?
[00:32:11] Russ Altman: I see.
[00:32:12] Aaron Lindenberg: And so, this is again a problem where you're searching through this massive phase space of possibilities. It's totally unobvious what the optimal kind of protocol is and, and so AI, we think, can, can help us to kind of solve these types of engineering problems.
[00:32:27] Russ Altman: Yeah, that, that strikes me as a good match because the optimal waveform, as you say, might be an incredibly weird looking, like, waveform, right? It might not be a sine or a cosine, it's just some crazy. And that's the kind of thing that AI can patiently try different options and tune and say, "Okay, here's the one. It looks random, but this is the one that might work." And it, it, it complements human capabilities where we're not always the best at thinking of crazy, crazy shapes.
[00:32:55] Aaron Lindenberg: Yep. That's exactly right.
[00:32:56] Russ Altman: Fantastic. Well, well, that's great and that's exciting. And before we end our conversation, I wanted to move to our segment that we call The Future in a Minute, and this is just where I ask you kind of five straightforward questions and you give me kind of short answers. And I'm wondering if you're ready for The Future in a Minute.
[00:33:13] Aaron Lindenberg: Let, let's begin.
[00:33:14] Russ Altman: Okay, let's do it. First question: What is one thing that gives you the most hope about the future?
[00:33:21] Aaron Lindenberg: Yeah, I, I think for me, it, a, a lot of it is about the really exciting opportunities kind of at the boundaries of fundamental and applied science. There's really exciting opportunities that can make our world a, a better place. And then maybe another quick answer to that, if I'm allowed, is, is, the, you know, my experience kind of working with young people, on these projects. These are kind of amazingly collaborative projects and, and, so seeing them in action also gives me a lot of hope for the future.
[00:33:51] Russ Altman: What's one thing you want people to walk away from this episode remembering?
[00:33:56] Aaron Lindenberg: Probably the, the importance of non-equilibrium science, the importance of, of really seeing things at the atomic scale and how they dynamically evolve, and how this is a really important problem that can affect our everyday lives.
[00:34:10] Russ Altman: Aside from money, what is one thing that you need for your research to succeed?
[00:34:17] Aaron Lindenberg: There's probably a few things. One is being stubborn. you know, kind of being, really, having the courage to kind of take on really challenging problems and, and not giving up in the, in the face of experiments always fail and, and, being able to push through these.
[00:34:36] And then the other really important aspect of doing science is, is about kind of the collaborative nature of it, right? And, and all the experiments that I talked about here, involved a huge number of people and would've been impossible without this, this kind of collaborative atmosphere.
[00:34:52] Russ Altman: If all goes well, what does the future look like?
[00:34:55] Aaron Lindenberg: I think, an opportunity, that I would be excited about if all goes well is, is, is people thinking about, problems in a scientific perspective. So if we could, you know, if, science... And, and actually when I say that, I don't mean thinking about things in a cold, logical kind of way all the time, but kind of approaching problems, and thinking creative, creatively about them, if, if, if people could approach this kind of way of thinking, then I think I would be optimistic about the future.
[00:35:31] Russ Altman: If you were starting over again and you needed to get your degree or certification in a different discipline, what would that be?
[00:35:40] Aaron Lindenberg: There's a few things that come to mind. One may be neuroscience. I've always been excited about the brain and, and how this works and the complexity and actually the dynamics that underlie it.
[00:35:50] Russ Altman: Pretty complicated material.
[00:35:51] Aaron Lindenberg: Pretty complicated material. And then another example might be music, actually. Music is also something that's really central to my life. And actually, I think a lot about, I think a lot about the parallels between doing science, and playing in a band. There's kind of amazing collaborative types of work. You have to work with other people in, in ways to creative, creatively make progress on something.
[00:36:13] Russ Altman: Thanks to Aaron Lindenberg. That was the future of ultra-fast materials. Thank you for listening to the show. Your ratings and reviews help inform the community and get people on board with The Future of Everything.
[00:36:25] So, if you haven't yet, please rate the podcast. We'd love to get a 5.0 if we deserve it. We don't want too much grade inflation. Or write a review telling us what we've done well and maybe where we could improve. We read every comment that you make, and we care deeply about those.
[00:36:43] Thanks so much. Thanks for tuning into this episode. Don't forget we have more than 300 episodes in our back catalog, so you can really spend quite a bit of time pondering the future of anything. You can connect with me on many social media, including LinkedIn, Threads, Bluesky, and Mastodon, where I'm @RBAltman or @RussBAltman. You can also follow Stanford School of Engineering @StanfordSchoolOfEngineering or more easily @StanfordENG.
