🔬Why There Is No "AlphaFold for Materials" — AI for Materials Discovery with Heather Kulik
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Why is there no "AlphaFold for Materials" and how does AI differ in materials discovery?
There's a school of thought that why should I bother to learn chemistry or physics or whatever when ChatGPT no has PhD level understanding of that anyway?
Chat GPT 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 metal complex and what that means is it's some combination of atoms and it's gonna bind to the metal and it's gonna 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.
Hi, we're really excited to have um Heather Kulik here. She's a professor of chemical engineering at MIT. Uh Heather has done some like amazing work in material science and computational chemistry. Um, 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 using applying them. to improve materials and under understanding of materials. And uh she has a lot of like really interesting opinions about what works and how do you approach these problems to get the most out of them. So um yeah, we're really excited to um have you here and um yeah, maybe to get started, can you just tell us about like one of the coolest things you've done in your opinion for a kind of an AI engineering audience?
Yeah, so uh my 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 what's surprising? What's what's sort of something from AI that like I wouldn't have already known? If I'm a really smart chemist or a really smart material scientist. And, you know, you make all these computational predictions. Has anyone actually made in the lab something that you predicted? Recently, I was I was able to do a really nice demonstration where the answer to both of those questions, you know, was very clear from the work.
So we were able to screen with artificial intelligence a set of Uh thousands, tens of thousands of materials where uh 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, um, that would make the polymer about four times tougher. And when we showed uh the design that AI had come up with to the experimentalists, they were really surprised. They would have never have come on this on their own. Um and then we were able to convince them to make it in the lab. And in fact it was it was this tougher material. And and where this has applications is if we can make plastics tougher then we, you know, can get more use out of them and it'll ultimately address some of the problems we have with overall
durability and use of plastics. So I think that's that's an example of some of the promise of AI and materials discovery.
Cool. So can you can you uh dig into a little bit? Um what was the surprising chemical discovery there?
So it's sort of hard for me to think about how to 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 kind of 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, you know, 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.
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Chapters
8 chapters
1
Why is there no "AlphaFold for Materials" and how does AI differ in materials discovery?
0:00–4:27
2
What was the breakthrough AI‑designed polymer that became four times tougher than expected?
4:27–9:18
3
Why does the 22‑atom ligand challenge expose the limits of current LLMs in chemistry?
9:18–13:23
4
How can active‑learning accelerate multi‑objective MOF design for CO₂ capture?
13:23–17:22
5
What are the data quality and dataset‑size challenges that prevent an AlphaFold‑style leap in materials science?
17:22–21:51
6
How can machine‑learned interatomic potentials be trusted when they sometimes predict unphysical behavior?
21:51–26:23
7
What role should academia play amid massive corporate AI investment in materials research?
26:23–30:21
8
How can listeners get involved – tools, datasets, and community initiatives for AI‑driven materials discovery?
30:21–34:58
Speakers
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