Millions of new materials discovered with deep learning (GNoME)

GNoME (Graph Networks for Materials Exploration) is Google DeepMind's demonstration that AlphaFold-style AI can be applied to materials discovery. The Nature 2023 paper describes a graph neural network trained on existing crystal structure data that predicts the stability of new hypothetical materials. In a single computational campaign, GNoME discovered 2.2 million stable crystal structures — more than the entire history of human materials discovery. Among these, 380,000 are predicted to be highly stable and potentially useful for real-world applications. The system focuses on inorganic crystals relevant to next-generation battery materials, solar cells, superconductors, and hard coatings. The paper demonstrates 'closed-loop' AI discovery: GNoME generates candidates, a robotic lab partner (in collaboration with Lawrence Berkeley National Laboratory) synthesizes and tests them, and results are fed back to refine predictions. Hassabis uses GNoME as evidence for the broader scientific discovery thesis: the same pattern-recognition capabilities that crack protein folding can be adapted to any domain where structure predicts function.