Machine Learning

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:

1. GNoME found 2.2 million new stable materials - equivalent to 800 years of normal discovery.
2. Of those, 380k were the most stable and candidates for validation.
3. 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.