Fluid Intelligence: Simulating Solutions with Tim Duignan

**Erik Torenberg** (0:00)
Hey everyone, Erik here.
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**Nathan Labenz** (0:35)
Hello, and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Erik Torenberg. Hello, and welcome back to The Cognitive Revolution. Today, I'm thrilled to share my conversation with Tim Duignan, an applied mathematician at the University of Queensland, Australia, who is using cutting-edge AI techniques for computational chemistry with the goal of deepening and perhaps one day revolutionizing our understanding of electrolyte solutions, from the humble and familiar salt water to the latest lithium batteries. Tim recently shared a short video of salt crystallizing in a water solution. He called this the most exciting result of his career, and this mesmerizing visualization reached nearly 2 million people on Twitter, sparking widespread fascination. In this conversation, we unpack all the techniques that went into making that magic moment. We explore how Tim simulates fundamental physical processes, and how by training neural networks on quantum mechanical simulation data, he's created models that can accurately predict the behavior of systems, of atoms and molecules, several orders of magnitude faster than conventional approaches. This breakthrough unlocks the potential for much larger and longer-lived simulations at various levels of scale, all with compute requirements that are remarkably modest by the standards of the foundation models that we're most familiar with today.
Throughout our discussion, Tim offers a master class crash course on computational chemistry. He explains concepts like coarse graining, that's a method to simplify complex systems by abstracting away detail thus ultimately average out, and also equivariance, a way of representing data that abstracts away from any particular coordinate system and thus helps models generalize better, all with remarkable clarity. We dive into the technical details of how these neural network potentials work, the surprising behaviors that they can capture, and the exciting potential for integrating these techniques into larger, more general AI systems in the future.
This conversation is a powerful reminder of how AI is accelerating scientific discovery in ways that we're only beginning to grasp. And for me, it's a compelling reason to believe that AI systems could become meaningfully superhuman in all sorts of weird and unexpected ways over the next couple of years.
As always, if you're finding value in the show, we'd appreciate it if you could take a moment and share it with a friend. You can always contact us via our website, cognitiverevolution.ai, where I'm now accepting resumes via a Google form, and I always welcome DMs on any social network. Now, I hope you enjoy this deep dive into the frontiers of computational chemistry and its intersection with AI, with Tim Duignan. Tim Duignan, welcome to the Cognitive Revolution.

**Tim Duignan** (3:26)
Hi, thanks for having me.

**Nathan Labenz** (3:29)
That's kind to say. Thank you for getting up super early. We're 14 hours apart, or 10, I guess, if we count the other way. And I appreciate you accommodating, and I know it's early there for you. I was attracted to your work in the first place, as I so often discover things on Twitter.
And you posted a tweet that probably is the most viral tweet you've ever posted, I imagine. It certainly went, probably traveled a lot farther than you expected, which basically showed a short video of salt crystallizing in water solution. So basically salt water with a little salt crystal.
And you said that this is the most exciting result of your career, and that inspired me to dig in and try to learn more.
Maybe for starters, give us a little bit of the sort of context. I think people, obviously, who tune into the cognitive revolution are paying attention to AI. They're probably not paying attention to the sorts of computational chemistry work that you're doing. So, you know, they may not even have any sense for like, we're still studying salt water, like that may be a sort of revelation to something. So can you give us the background of what exactly it is you're studying? What are the sort of fundamental questions you're trying to answer? And then we can get into how you're using some cool AI techniques to approach those questions.