Searching the space of materials.
GNoME used graph networks to search for energetically favorable crystal structures.
Original sources ↓ · Revision history ↓
Experimental · source published 2023-11-29
The human problem
The space of possible inorganic materials is enormous.
The prior constraint
Candidate materials are evaluated with expensive calculations and experiments.
AI’s actual role
Graph networks guided computational screening.
The documented result
The study greatly expanded the set of predicted stable structures.
Why it may matter
A resource of candidates for materials researchers.
Limitations
Predicted stability does not establish synthesizability, novelty, or practical usefulness.
Unresolved questions
Validate candidates through careful synthesis and characterization.
Source history & evidence assessment
- Maturity
- Experimental
- Claim confidence
- unassessed
- Event date
- Not recorded
- Source published
- 2023-11-29
- Captured
- Not recorded
- Last source review
- 2026-09-05
- Editorial method
- Original source check
- Place / relevance
- London, United Kingdom · institution-location
Legacy source check; no named human reviewer is recorded in this projection.
Maturity describes the tested or operational setting. Confidence describes support for the particular claim; one does not determine the other.
Original sources
Scaling deep learning for materials discovery ↗ · paper
Institutions: Google DeepMind
Explore the underlying question
Related developments
Editorial connections between distinct settings and results; these links do not imply replication.
Revision & correction history
No corrections recorded.
