🔬Why There Is No "AlphaFold for Materials" — AI for Materials Discovery with Heather Kulik

**Brandon Anderson** (0:00)
There's a school of thought that, why should I bother to learn chemistry or physics or whatever when ChatGPT, you know, has PhD level understanding of that anyway.

**Heather Kulik** (0:13)
ChatGPT is super good at Wikipedia level chemistry knowledge. I'm really interested in molecular design. Like, how do you find a new ligand that can go into a transition malcomplex? And what that means is that some combination of atoms, and it's going to bind to the metal and it's going to change its properties. The thing I constantly do every time an LLM is updated is I just ask it, please design me a ligand that has 22 atoms. I can never get an answer that has 22 atoms.

**RJ Honicky** (0:46)
Hi, we're really excited to have Heather Kulik here. She's a professor of chemical engineering at MIT. Heather has done some amazing work in material science and computational chemistry. But we're particularly excited to have her today because she has, for almost her entire career, been working on the intersection of using data-driven methods, AI, and applying them to improve materials and understanding materials.
She has a lot of really interesting opinions about what works and how do you approach these problems to get the most out of them. So, yeah, we're really excited to have you here. And yeah, maybe to get started, can you just tell us about one of the coolest things you've done, in your opinion, for an AI engineering audience?

**Heather Kulik** (1:36)
Yeah. So my group, we work a lot in accelerated discovery of new materials. When I first started out, we were just really using AI to make predictions we'd normally make with computational models, just make them faster. But the question I would often get when we were doing that, was, okay, but what's surprising? What's something from AI that I wouldn't have already known if I were a really smart chemist or a really smart material scientist?
You make all these computational predictions, has anyone actually made in the lab something that you predicted? Recently, I was able to do a really nice demonstration where the answer to both of those questions was very clear from the work. So we were able to screen with artificial intelligence a set of thousands, tens of thousands of materials where each individual experiment if it were done in the lab would have taken months to years. And through AI, we uncovered this sort of unexpected chemical phenomenon that led to an emergent property in what's known as a polymer network, so plastics, that would make the polymer about four times tougher. And when we showed the design that AI had come up with to the experimentalists, they were really surprised. They would have never come on this on their own. And then we were able to convince them to make it in the lab. And in fact, it was this tougher material. And where this has applications is if we can make plastics tougher, then we can get more use out of them. And it will ultimately address some of the problems we have with overall durability and use of plastics. So I think that's an example of some of the promise of AI and materials discovery.

**Brandon Anderson** (3:23)
Cool. So can you dig into a little bit? What was the surprising chemical discovery there?

**Heather Kulik** (3:30)
So it's sort of hard for me to think about how to explain it without getting too deep into the chemistry. But basically, these are molecules that have to break apart. And when they break apart, they make the overall structure that they're in tougher. So a little part of the material breaks and that helps to dissipate the force. Normally, the way you would think about making it easier to break apart, these small molecular components might be to create a hinge, so they can peel open instead of sliding apart. But what we discovered was that there was a fully quantum mechanical phenomenon. There was really no way for us to predict this based on anything else, where the electrons just move around in a different way, so that at this moment where the molecule is going to break apart, it's a lot more stabilized. These types of concepts, they're sort of similar to what's kind of known about how catalysts and enzymes work, but it had never before been shown in these polymer materials.

**Brandon Anderson** (4:30)
So this is sort of like the fuse in the Bay Bridge that sort of allows the bridge to keep its structural integrity during an earthquake by having a controlled break. Is that kind of?

**Heather Kulik** (4:43)
Yeah, yeah. So we weren't the first ones to discover that phenomenon on its own. The general phenomenon that putting little places that could break to make the network stronger. That was published in Science Magazine a couple years ago, but the specific way we came up with to design the material to do this, that was our new contribution.