Material Progress: Developing AI's Scientific Intuition, with Orbital Materials' Jonathan Godwin & Tim Duignan
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What is AI’s role in accelerating material discovery and why does it matter?
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 and machine learning algorithms are just very good at doing that.
The thing that completely blew my mind was, you know, training on small inorganic crystals like Twenty atom systems. to then simulate a protein through uh 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.
The hope is that if you can s simulate a ton of things, you start to make important and new discoveries and find new things out just by looking at them, right? Which is very exciting.
By the time we get to making a decision about what to make, we've answered ninety percent of the questions that we need to in order to feel confident that we're gonna have that sort of material.
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. And in fact, we don't even know some of the most basic questions about it.
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 materials science, and Tim Dagnan, 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 language 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 signaling 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.
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Chapters
8 chapters
1
What is AI’s role in accelerating material discovery and why does it matter?
0:00–15:08
2
How do Orbital Materials’ message‑passing neural networks differ from traditional ML models?
15:08–27:13
3
Why are diffusion models key to generating new materials and simulating dynamics?
27:13–40:27
4
What insights did the potassium ion‑channel simulation reveal about water transport?
40:27–50:46
5
How does Orbital’s workflow combine generative design with high‑fidelity simulation?
50:46–1:02:18
6
What are the current challenges and limits when scaling simulations to larger systems?
1:02:18–1:13:10
7
How is AI being applied to carbon‑capture materials for data‑center cooling?
1:13:10–1:24:12
8
What does the future look like for AI‑augmented scientific discovery and job satisfaction?
1:24:12–1:34:29
Speakers
2 identifiedMore from "The Cognitive Revolution"
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