GNoME predicts 2.2 million new crystals, 380,000 stable, but novelty and usefulness are disputed
DeepMind's GNoME (Nature, Nov 2023) used graph neural networks and active learning with DFT to predict 2.2 million new inorganic crystal structures, 380,000 of them computed to be stable. DeepMind called it '800 years' worth of knowledge'. Solid-state chemists later found 'scant evidence' of compounds that are novel, credible and useful.
Key facts
- 2.2M new structures; 380k predicted stable; ~400k added to the Materials Project
- DeepMind: over 700 had already been independently synthesised by other groups
- Cheetham & Seshadri (Chem. Mater., Apr 2024): 'scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility'
- GNoME lead Ekin Doğuş Çubuk later co-founded Periodic Labs (2025)
Science result
- Field
- materials / inorganic crystal discovery
- Problem
- Discovering new stable inorganic materials
- Result
- An order-of-magnitude expansion of computationally predicted stable crystals (380,000).
- AI system
- GNoME
- Human role
- Human-designed pipeline; predictions automated
- Verification
- Peer-reviewed in Nature; DFT-computed stability; limited experimental synthesis
- Status
- disputed
- Why surprising
- The scale ('800 years of knowledge') was striking, but so was the pushback from chemists about what counts as a new material.
What happened
DeepMind scaled ML-guided materials screening by orders of magnitude and released the predicted structures to researchers.
Why it matters
It is the largest AI materials-prediction effort, and a leading example of the gap between computational "discovery" and a useful new material.
Changelog
- 2026-09-29: created
- 2026-09-29: linked Periodic Labs entry (co-founded by GNoME lead Çubuk)
Related events
- Berkeley's A-Lab claims 41 new materials from autonomous synthesis; after critiques Nature corrects it to 36 'inorganic' (not 'novel') materials ★★★
- Periodic Labs launches with a $300M seed round to build AI scientists with autonomous labs ★★★
- Microsoft's MatterGen generates materials to order; flagship result later challenged as a known compound ★★★
- Machine-learning screen predicts two new kagome superconductors, confirmed in the lab ★★
- Lila Sciences' AI-run lab screens 2,942 catalysts and finds iridium- and ruthenium-free palladium oxides for green hydrogen ★★★
Sources (3)
- paperScaling deep learning for materials discovery (Nature)
- officialDeepMind: Millions of new materials discovered with deep learning
- discussionCheetham & Seshadri critique (Chemistry of Materials)
id: 2023-11-29-gnome-millions-of-materials · updated 2026-09-29 · open in the interactive timeline