{"data":{"event":{"id":"dcc55add-fde7-4165-94cd-39a00695425a","slug":"gnome-inorganic-crystal-catalog","title":"GNoME expands the catalog of computationally stable inorganic crystals","short_summary":"DeepMind’s GNoME graph networks, published in Nature, identified about 381,000 new convex-hull-stable inorganic structures after large-scale DFT filtering of more than 2.2 million candidates.","full_description":"On 29 November 2023, Nature published “Scaling deep learning for materials discovery.” Graph Networks for Materials Exploration (GNoME) used active learning and DFT to propose 2.2 million structures predicted to be stable relative to prior datasets; after competition on the hull, 381,000 new entries remained. The authors released structures publicly and compared a subset to experiment and r2SCAN calculations. Independent experimental realization of most candidates remains incomplete, and stability is defined computationally.","ledger_type":"benefit","primary_domain_id":"5967400b-72d1-43e0-8355-37061fd039bb","event_status":"verified","event_date":"2023-11-29T00:00:00.000Z","discovery_date":"2023-11-29T00:00:00.000Z","first_published_date":"2023-11-29T00:00:00.000Z","last_reviewed_date":"2023-11-29T00:00:00.000Z","geographic_scope":"Global materials-research community","affected_population":"Computational and experimental materials scientists using the released catalog","base_impact_tier":3,"base_score":"10.00","attribution_multiplier":"0.9000","evidence_multiplier":"0.7000","realization_multiplier":"0.5000","durability_multiplier":"0.7000","current_event_score":"2.205000","confidence_level":"high","score_explanation":"Major within materials informatics (tier 3), not a deployed device. High attribution to GNoME. Peer-reviewed computational evidence (0.70). Realization discounted because most materials are un-synthesized. Medium durability as hulls and functionals are revised.","methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","original_methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","published_at":"2023-11-29T00:00:00.000Z","created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z","flags":[],"domain_name":"Materials Science","domain_slug":"materials-science","methodology_version":"0.1"},"contributions":[{"id":"5c7dd0cb-0032-43f3-8b90-aa714bca19ef","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","model_id":"8fffdd7a-d88d-4946-8467-759f6baadcfe","role_description":"Graph networks filtered candidate crystals later evaluated with DFT.","attribution_multiplier":"0.9000","credit_share":"1.0000","contribution_score":"2.205000","attribution_rationale":"The paper attributes the scaled discovery loop to GNoME predictions plus DFT.","attribution_confidence":"medium","first_used_date":"2023-11-29T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-13T20:47:20.075Z","model_slug":"gnome","model_name":"GNoME","identity_class":"named_release","is_internal":false,"family_name":"GNoME","family_slug":"google-deepmind-gnome","organization_name":"Google DeepMind","organization_slug":"google-deepmind"}],"sources":[{"id":"0346b4fa-443e-4462-838c-5402e49b38e8","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","url":"https://www.nature.com/articles/s41586-023-06735-9","canonical_url":"https://www.nature.com/articles/s41586-023-06735-9","source_type":"peer_reviewed","publisher":"Nature","author":null,"publication_date":"2023-11-29T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"Scaling deep learning for materials discovery","excerpt":null,"content_hash":null,"source_reliability_class":"high","is_primary_source":true,"is_independent":true,"is_peer_reviewed":true,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"},{"id":"284b2cd6-5859-4ff0-a08f-fa07a8e1bdf3","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","url":"https://github.com/google-deepmind/materials_discovery","canonical_url":"https://github.com/google-deepmind/materials_discovery","source_type":"developer_announcement","publisher":"GitHub / Google DeepMind","author":null,"publication_date":"2023-11-29T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"Materials Discovery: GNoME datasets and models","excerpt":null,"content_hash":null,"source_reliability_class":"low","is_primary_source":true,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"}],"claims":[{"id":"7daf96a4-67c5-4ec4-a5ed-32d40ea5ee98","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","claim_text":"GNoME-guided search added on the order of 381,000 new computationally stable crystals to the updated convex hull.","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"},{"id":"aac70037-efdb-4593-ad07-025eb1d01efc","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","claim_text":"Most GNoME candidates have been synthesized and used in commercial devices.","claim_type":"outcome","claim_status":"disputed","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"}],"revisions":[{"id":"15964e2c-48a8-463b-9fbd-a776e6cff0dd","event_id":"dcc55add-fde7-4165-94cd-39a00695425a","model_id":null,"previous_score":"0.000000","new_score":"2.205000","previous_factors":{},"new_factors":{"evidence":0.7,"base_score":10,"durability":0.7,"attribution":0.9,"realization":0.5},"change_reason":"Initial adjudicated seed score under methodology 0.1.","trigger_type":"seed","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-13T20:47:20.075Z"}],"secondary":[{"name":"Chemistry","slug":"chemistry"},{"name":"Energy","slug":"energy"}]},"methodology_version":"0.1"}