# Searching the space of materials.

GNoME used graph networks to search for energetically favorable crystal structures.

Canonical: https://brightaifuture.com/discoveries/gnome
Format: discovery
Source publication: 2023-11-29
Bright publication: 2026-09-05
Substantive update: None recorded
Evidence and review: Experimental; confidence: unassessed; source-checked; legacy source check. Legacy source check; no named human reviewer is recorded in this projection.

## 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.

## Provenance and history

{
  "dates": {
    "eventDate": null,
    "publicationDate": "2023-11-29",
    "captureDate": null,
    "lastReviewedDate": "2026-09-05"
  },
  "provenance": {
    "origin": "legacy-projection",
    "externalId": "gnome"
  },
  "revisions": [],
  "corrections": []
}

## Original sources

- [Scaling deep learning for materials discovery](https://www.nature.com/articles/s41586-023-06735-9)

## Continue exploring

- [Frontier](https://brightaifuture.com/worlds/frontier)
- [Where can human judgment go with a new instrument?](https://brightaifuture.com/threads/discovery)
- [Extended story and historical record](https://brightaifuture.com/stories/searching-the-space-of-materials)
- [Open this record in the interactive world](https://brightaifuture.com/?event=gnome)