{ "id": "AIBD-0261", "title": "GNoME predicts 2.2 million crystal structures, of which few may be useful new compounds", "summary": "Google DeepMind's GNoME proposed 2.2 million crystal structures (380,000 stable); independent reviews (Cheetham & Seshadri 2024, Kurlin 2026) find duplicates and scant evidence of useful compounds.", "date_observed": "2023-11-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 29 November 2023 Google DeepMind published GNoME in Nature. It reported 2.2 million structures below the convex hull, calling it “an order-of-magnitude expansion in stable materials known to humanity”. It stated that 736 of the stable structures had already been independently realized. The company blog adds that the Berkeley Lab A-Lab synthesized more than 41 materials from the predictions. Cheetham and Seshadri (UC Santa Barbara, Chem. Mater., 8 April 2024) examined the listings. They reported “scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility”. 404 Media (11 April 2024) quoted Cheetham that the paper “falls way short” as a practical contribution and DeepMind that it stands by all claims. C&EN (15 October 2026) reported an arXiv analysis by Vitaliy Kurlin (Liverpool). It found more than 15% near-duplicates within GNoME, about 24% duplicating Materials Project entries and about 17% close to ICSD entries. Leslie Schoop (Princeton) called some entries “completely unrealistic”. The Chem. Mater. critique itself was not available. Its abstract on Europe PMC (PMC11044265) is among the sources.", "category": "general benefit", "unexpected": false, "purpose": "Predict stable inorganic crystal structures at scale to accelerate materials discovery", "system": "GNoME (Graph Networks for Materials Exploration)", "model_version": null, "developer": "DeepMind", "deployer": "DeepMind (dataset released openly; stable structures contributed to the Materials Project)", "location": "Mountain View, California, USA", "domain": "Materials science", "segment": "Materials researchers", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "Independent reviewers dispute the novelty and utility of the predicted structures.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary. A dataset release." }, { "group": "organizational", "depth": null, "reason": "Use by the Materials Project and A-Lab is reported only by the vendor. Independent reviewers find scant utility." }, { "group": "societal", "depth": null, "reason": "No demonstrated downstream technology. Novelty of the predictions is disputed." } ], "ai_role": { "status": "unassessed", "statement": "The graph-network model is the vendor's. Its predictions are documented in a vendor-authored Nature paper and blog. Independent reviewers (Cheetham & Seshadri 2024; Kurlin 2026 as reported by C&EN) report near-duplicates, unrealistic compounds and little evidence of useful novel materials. Benefit therefore rests on the vendor's account and is contested." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Nature 624:80-85 (2023)", "title": "Scaling deep learning for materials discovery", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10700131/", "date": "2023-11-29", "written_by": "makers", "quote": "Our work represents an order-of-magnitude expansion in stable materials known to humanity." }, { "kind": "press release", "citation": "Google DeepMind blog, 29 Nov 2023", "title": "Millions of new materials discovered with deep learning", "url": "https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/", "date": "2023-11-29", "written_by": "makers", "quote": "External researchers in labs around the world have independently created 736 of these new structures experimentally in concurrent work" }, { "kind": "peer-reviewed study", "citation": "Cheetham AK, Seshadri R. Chem. Mater. 36:3490-3495 (2024)", "title": "Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11044265/", "date": "2024-04-08", "written_by": "independent", "quote": "unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility" }, { "kind": "news report", "citation": "Koebler J. 404 Media, 11 Apr 2024", "title": "Google Says It Discovered Millions of New Materials With AI. Human Researchers Say...", "url": "https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/", "date": "2024-04-11", "written_by": "independent", "quote": "the Google paper falls way short in terms of it being a useful, practical contribution to the experimental materials scientists." }, { "kind": "news report", "citation": "Chemical & Engineering News, 15 Oct 2026", "title": "Google DeepMind AI crystallography database includes near duplicate structures and unrealistic compounds", "url": "https://cen.acs.org/research-integrity/Google-DeepMind-structures-near-duplicates-questionable/104/web/2026/10", "date": "2026-10-15", "written_by": "independent", "quote": "My biggest problem with GNoME is that there are materials in there that are completely unrealistic." } ], "replication": { "produced_by": [ "Graph neural network trained on existing crystal databases with iterative active learning", "High-throughput DFT stability checks of candidates", "Open release of the predicted structures" ], "must_hold": [ "Predicted structures are genuinely novel rather than near-duplicates of known entries", "Predicted compounds are synthesizable and have useful properties", "Independent experimental confirmation of utility" ], "echoes": [ { "date": "2024-04-08", "setting": "Independent review of GNoME listings by Cheetham and Seshadri (UC Santa Barbara)", "model_version": null, "outcome": "did not recur", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11044265/" }, { "date": "2026-10-09", "setting": "Kurlin et al. arXiv analysis of the November 2024 GNoME update, reported by C&EN", "model_version": "GNoME dataset update, Nov 2024", "outcome": "did not recur", "source": "https://cen.acs.org/research-integrity/Google-DeepMind-structures-near-duplicates-questionable/104/web/2026/10" } ] }, "paired_incident": null, "headline": "GNoME's 2.2 million predicted crystals may include useful compounds", "metric": "2.2 million crystal structures (380,000 stable)", "comparator": { "against": "stable crystals previously identified by computational studies before GNoME", "before": "48,000 computationally stable materials", "after": "a total of 421,000 stable crystals", "basis": "measured", "quote": "Given that discovered materials compete for stability, the updated convex hull consists of 381,000 new entries for a total of 421,000 stable crystals, representing an-order-of-magnitude expansion from all previous discoveries.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10700131/" }, "later": { "status": "reversed", "date": "2024-04-08", "note": "Cheetham and Seshadri's April 2024 perspective in Chemistry of Materials examined the GNoME listings and found scant evidence of novel, credible and useful compounds, identifying every one of ten sampled 'stable structure' entries in the ICSD; C&EN reported further duplicate findings by Kurlin in October 2026.", "quote": "We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11044265/" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10700131/; https://pmc.ncbi.nlm.nih.gov/articles/PMC11044265/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://en.wikipedia.org/wiki/Google_DeepMind", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study, two independent news reports, the makers' peer-reviewed study and the makers' press release. 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