Material Progress: Developing AI's Scientific Intuition, with Orbital Materials' Jonathan Godwin & Tim Duignan

**Erik Torenberg** (0:02)
Before we dive into today's episode, I want to tell you about a new show from Turpentine called Modern Relationships. On the season ahead, I sit down with power couples in tech and leading relationship thinkers to explore how ambitious people actually make partnerships work. Whether you're dating in a relationship or just curious how technology is reshaping modern love, I think you'd enjoy this on your feed. Our first episode features founders funds Delian Asparuhov and tech researcher Nadya Asparuhov, who take us through their evolution from dating to marriage to parenthood, with absolutely no filter on the challenges and growth along the way. You can find Modern Relationships wherever you get your podcasts. Now, on to today's episode.

**Tim Duignan** (0:38)
Finding efficient ways of keeping track of the important information and losing the unimportant information is just a central problem in a lot of physical modeling, and I think AI machine learning algorithms are just very good at doing that.

**Jonathan Godwin** (0:50)
The thing that completely blew my mind was training on small inorganic crystals, like 20 atom systems, to then simulate a protein through out-of-the-box generalization. That's telling you that we're learning something really fundamental at that small scale, which I don't think anyone had ever expected.

**Tim Duignan** (1:09)
The hope is that if you can simulate a ton of things, you start to make important and new discoveries and find new things out just by looking at them, which is very exciting.

**Jonathan Godwin** (1:17)
By the time we get to making a decision about what to make, we've answered 90% of the questions that we need to in order to feel confident that we're going to have that sort of material.

**Tim Duignan** (1:28)
The real key challenge there had been these potassium ion channels, which no one has really been able to fully understand, unfortunately, using experimental techniques or traditional computation. In fact, we don't even know some of the most basic questions about it.

**Nathan Labenz** (1:44)
Hello, and welcome back to The Cognitive Revolution. Today, my guests are Jonathan Godwin, founder and CEO of Orbital Materials, which is pioneering the application of AI to material science, and Tim Duignan, who was previously here to discuss his work on the simulation of electrolyte solutions, and who's since joined Orbital Materials as a researcher. Material science underpins virtually every aspect of modern life. From the semiconductors that power our devices, to the batteries and solar panels driving the clean energy transition, advances in materials have been at the heart of human progress for the last century at least. The challenge has been that discovering and developing new materials has always been painstakingly slow, traditionally relying on trial and error, and scientists' hard-won intuitions developed over decades. And more recently with the shift to computer simulation, still requiring huge computing power to simulate even small molecular systems for short time intervals.
Orbital Materials aims to dramatically accelerate this process with, of course, AI. Their immediate focus is on developing novel materials for data centers, both to improve efficiency and to capture carbon emissions, but their technical breakthroughs could unlock advances across clean energy, electronics, medicine, and beyond. Their technical approach is really quite fascinating. Using an architecture called message-passing neural networks, which are trained on small crystal structures, and which, because they don't use positional embeddings like large English models do, are capable of scaling up indefinitely with computing power, they can design new materials with specific target properties via a diffusion process, and also predict the forces between atoms orders of magnitude faster than numerical methods can, which allows them to simulate larger systems for longer. They recently demonstrated the power of this approach by simulating a potassium ion channel, a critical protein that controls electrical signalling in our cells by selectively allowing potassium ions to pass through cell membranes. Despite its importance in everything from heartbeats to brain functions, fundamental questions about how this channel works have remained unanswered for decades. And while Tim's recent work still needs to be experimentally confirmed by the broader research community, his simulations were able to show a level of detail in the mechanism that was never before seen, and which does help explain previously inexplicable data. The implications for biology and medicine are significant, but perhaps more important still, this work illustrates a critical phenomenon that we are seeing time and again as AI is applied to the different branches of science. Namely, that neural networks seem to have the ability to develop a sort of intuitive physics in virtually any problem space. Just as humans can catch a ball without explicitly calculating its trajectory, these AI systems are developing efficient shortcuts for predicting complex physical phenomena. Whether that's material properties, protein folding and interactions, weather forecasts, single cell transcriptomes, or even the evolution of human brain states. For me, this is the clearest reason to believe that superhuman intelligence is not just possible, but increasingly likely. For human scientists, meanwhile, this means a shift away from hypothesis generation and toward more validation and implementation work. And while that might mean lower job satisfaction, the potential to dramatically accelerate scientific progress and more effectively address critical global challenges, for me, makes it a worthy trade-off. And again reminds us that we might soon need to look beyond our work for meaning. As always, if you're finding meaning in the show, we'd appreciate it if you'd share it with friends. You can also write us a review on Apple or Spotify, and we love to read your comments on YouTube. We value your feedback and suggestions too, and encourage you to leave them either via our website, cognitiverevolution.ai, or you can always DM me on your favorite social network. For now, I hope you enjoyed this look at how AI is transforming material science, and by extension, how it might fundamentally reshape and accelerate scientific progress in general. With Jonathan Godwin and Tim Duignan of Orbital Materials. Jonathan Godwin, founder and CEO of Orbital Materials, and returning guest and now researcher at Orbital Materials, Tim Duignan. Welcome to The Cognitive Revolution.