Materials discovery is critical but tough. New materials enable big innovations like batteries or LEDs. But there are ~infinitely many combinations to try. Testing for them experimentally is slow and expensive.
So scientists and engineers want to simulate and screen materials on computers first. This can check way more candidates before real-world experiments. However, models historically struggled at accurately predicting if materials are stable.
Researchers at DeepMind made a system called GNoME that uses graph neural networks and active learning to push past these limits.
GNoME models materials’ crystal structures as graphs and predicts formation energies. It actively generates and filters candidates, evaluating the most promising with simulations. This expands its knowledge and improves predictions over multiple cycles.
The authors introduced new ways to generate derivative structures that respect symmetries, further diversifying discoveries.
The results:
- GNoME found 2.2 million new stable materials - equivalent to 800 years of normal discovery.
- Of those, 380k were the most stable and candidates for validation.
- 736 were validated in external labs. These include a totally new diamond-like optical material and another that may be a superconductor.
Overall this demonstrates how scaling up deep learning can massively speed up materials innovation. As data and models improve together, it’ll accelerate solutions to big problems needing new engineered materials.
TLDR: DeepMind made an AI system that uses graph neural networks to discover possible new materials. It found 2.2 million candidates, and over 300k are most stable. Over 700 have already been synthesized.
Full summary available here. Paper is here.
Meanwhile people discuss how Google wasn’t capable of striking back at OpenAI with a good conversational agent, “thus loosing its status as ML behemoth”. It’s interesting how LLMs bring out accelerationist and xrisk debates of science fiction/fabrication, and at best debates on economy, while research on materials and climate science warms few minds and arts (at least looks so on X/Mastodon/Reddit)
I think a lot of people (myself included) don’t really think Google fell behind in the ml space but rather didn’t manage to capitalize on their own invention.
It’s more a business side issue rather than a R&D one.
Also deep mind, although owned by Google is a separate entity.
My guess is that people tend to anthropomorphise many things, especially those they don’t really understand. A language model comes across as “smart” because we can converse with it in human ways. Thinking about material discovery is so distant for most that they don’t really grasp impressive and impactful this work can be.
Now, to me what’s happening here is extremely impressive and I’ve been a fan of deepmind their stem related work for a while. Seems like we could see some big acceleration in stem fields over next years, which will arguably have a bigger impact on people their lives than the things LLMs are used for right now.
Engineers comprise 0.06% of US population for example. Managers around 20%. Also, narrow AI systems aren’t so fascinating.
I agree. I honestly don’t see the advantage of LLMs beyond a better Google search response. It needs to be said that ai is so much more than a chat bot.
Google has made significant strides in many meaningful ways that shouldn’t be understated.
Alpha* ( or maybe MCTX ) is in my mind one of those advances that is truly bringing us closer to real world improvements. I shouldn’t discount GNNs like used here too but I don’t think this is their big win like MCTX is yet.
Overall maybe a lot less hype and a lot more real world application is what ai and ml need now.