The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; 7 of the 25 sources its claims cite were checked for support.
Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery
Open accessAbstract
The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This perspective scrutinizes the Google/Merchant et al. GNoME claims for AI-driven materials discovery, arguing that the predicted compounds largely fail the trifecta of novelty, credibility, and utility. The authors find scant evidence for genuinely new materials and call for greater incorporation of solid-state chemistry and crystallography domain expertise.
Key findings
- In the GNoME Explorer archive, the authors examined the first 250 of 2047 entries and found recurring problems: unconventional composition formatting, improbable oxidation states, structures with F₂ molecules or unlikely rare-earth ordering, and many obvious analogues of known compounds.
- In a random sample of 10 compounds from the 384,870-entry Stable Structure database, all 10 structures could be identified in the ICSD, usually with higher symmetry; only one had the same space group.
- Space-group statistics differ sharply from the ICSD: the top four space groups in the Stable Structure database are all non-centrosymmetric and account for ∼34% of structures, whereas in the ICSD only one non-centrosymmetric space group is in the top 24 and accounts for only 1%. Pnma is ranked 16th in the Stable Structure database at 1.56%, and P21/c is found in only 0.7%, versus ∼8% each in the ICSD.
- The database includes many impractical radioactive-element compounds: 18,138 compounds of Pm, Ac, and Pa in the large Stable Structure database, plus 23,529 entries containing Tc, Np, and Pu; 27 actinium compounds appear in GNoME and approximately 6754 in the Stable Structure database. The authors “have yet to find any strikingly novel compounds” in the GNoME and Stable Structure listings.
Methods
- Perspective/commentary by experimental materials chemists, evaluating the Merchant et al. Nature 2023 article and its associated GNoME Explorer and Stable Structure databases.
- The authors examined the first 250 entries in the 2047-entry GNoME Explorer archive, then randomly selected 10 compounds from the 384,870-entry Stable Structure database for comparison with ICSD entries.
- They analyzed space-group statistics of the 384,870 Stable Structure entries and compared them with ICSD distributions reported by Urusov and Nadezhina, using ICSD search tools to identify structural analogues.
Limitations
- The paper states its GNoME Explorer scrutiny was not comprehensive and was limited to the first 250 entries; the Stable Structure analysis used only 10 randomly selected compounds.
- The authors note that the computational tools to treat disorder and finite-temperature behavior exist but are computationally intensive and not scalable in the Merchant et al. approach.
- Evident from the text: the analysis is a perspective based on database inspection and ICSD comparison, not experimental synthesis or direct testing of the predicted compounds; the authors also acknowledge that some novel compounds likely exist among the 384,870 compositions even though they did not find striking examples.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Verification
Claims the paper makes
7 of 25 cited sources checked · 5 supported · 1 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“One of the lessons from that study was that most breakthroughs are not achieved by design or serendipity, but by exploring opportunities in the extensive repository of compounds that are already known.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Introduction
What the sources say
- Supportedcites [5] source
The Abstract says breakthroughs based on design principles occur in a small number of cases, serendipitous breakthroughs are rare, and most commonly a breakthrough repurposes an already-known compound. The Outlook describes the extensive repository of known compositions and structures that materials scientists can explore for new functionalities. (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“the predictions in the contribution of Merchant et al.1 are solely of crystalline inorganic compounds”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Introduction
What the sources say
- Supportedcites [1] source
The Main section defines the discovery target as inorganic crystals, and the Discovered stable crystals section describes the predicted structures as crystals. The paper’s additional ionic-conductivity predictions concern inorganic bulk crystals and solid-state electrolytes (Scaling up learned interatomic potentials > Screening solid-state ionic conductors). Our copy identifies no predictions of non-crystalline or organic compounds. (LLM, conf=0.90)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“many of the entries are based upon the ordering of metal ions that are unlikely to be ordered in the real world.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The above comments about cation ordering in inorganic compounds also apply to a very large number of the intermetallic phases, leading to many other examples of unlikely predictions.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“We have found that the space groups of the predicted compounds (Table 1) have a distribution that is strikingly different from those in the Inorganic Crystal Structure Database (ICSD),15”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Space Group Analysis
What the sources say
- Not checked — no full text of the sourcecites [15] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In fact, there are 18,138 compounds of such radioactive elements in the large Stable Structure database, including those of Pm, Ac, and Pa.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It also must be recognized that a large fraction of the 384,870 compositions adopt structures that are already known and can be found in the ICSD database.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“several of the Stable Structure entries involve unit cell parameters that are incompatible with the stated space groups, i.e., display pseudosymmetry.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We were able to identify the structure of every one of the 10 Stable Structure entries in the ICSD database, albeit usually with a space group of higher symmetry than that in the AI database.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 27
- Probably not a substantial claim. 13
- The quote only partly states this claim. 2
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“We would respectfully suggest that the work by Merchant et al.1 does not report any new materials but reports a list of proposed compounds.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
What the sources say
- Disputedcites [1] sourceExperimental — accuracy not yet measured
The paper’s “Discovered stable crystals” section explicitly reports 381,000 entries on its updated computational stability hull as “newly discovered materials,” contrary to the claim that it reports no new materials. These are computational discoveries, not materials newly synthesized by the authors; the validation section says 736 structures matched crystals independently created by other researchers. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Disputed
Every source the paper cites for this claim was checked and supports it.
In the paper
“Such hexaborides are well-known and are found for a wide range of elements, such as the alkaline earths and rare earths.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Supportedcites [6] source
The section on three-dimensional boron frameworks (p. 10) describes numerous isomorphous borides of the CaB6 type. Table 9 (p. 11) lists hexaborides of the alkaline earths Ba, Sr and Ca and the rare earths Yb, La, Ce and Nd, supporting the stated range of elements. (LLM, conf=0.93)
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“while further structural details of more than 2000 of these new compounds have been placed in the GNoME Explorer archive3 within the Materials Project.4”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Introduction
What the sources say
- Not matchedcites [3] https://next-gen.materialsproject.org/materials/gnome (accessed November 29, 2023).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites [4] source
Citation 4 identifies the Materials Project as the platform; the GNoME Explorer archive is separately cited as reference 3. Section III (page not provided) describes the Materials Project’s web applications for accessing materials data, supporting the platform identification. The count and GNoME deposit are not attributed to citation 4. (LLM, conf=0.88)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This enabled us to find an obvious analogue of Ga2Nb20Os8 in the ICSD by searching for structures in space group P42/mnm with lattice parameters close to a = 9.9 Å and c = 5.1 Å, pointing us to Ge3.9Nb20.4Rh5.9, which is one of the well-known Frank–Kasper sigma (σ) phases.20,21”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
What the sources say
- Partly supportedcites [20] sourceExperimental — accuracy not yet measured
The Introduction identifies σ phases as Frank–Kasper structures, and the Results and discussion reports a Nb–Rh–Ge σ phase in P42/mnm with lattice parameters close to those cited. But the paper gives its composition as Nb20.4Rh5.7Ge3.9, not Ge3.9Nb20.4Rh5.9; it also does not establish the citing authors’ ICSD search or comparison with Ga2Nb20Os8. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [21] source
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In the case of Ac4P8S28Tl8, the similarity to the structure of La2P4S14Rb424 is not immediately apparent from the unit cells. However, we would expect Ac compounds would be isomorphous with their La analogues. Tl1+ and Rb1+ are also close in size and identical in their charge states. The similarity of the structures then becomes evident.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
What the sources say
- Supportedcites [24] source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Ac compounds would be isomorphous with their La analogues.
- Rests on an assumption: Tl1+ and Rb1+ are close in size and identical in their charge states.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another common claim among the database entries is the prediction of new compounds based upon radioactive elements that do not occur in usable quantities in nature.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Oxidation states are often inconsistent with known materials, or structural features are obscure or entirely unprecedented in known materials.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“TbSmF30 (mp-3170019) defies all the usual rules of valence and contains an improbable ordering of Tb3+ and Sm3+ (see below). Further inspection of the structure reveals that it contains large numbers of F2 molecules, some isolated and others acting as linkers between LnF9 polyhedra (where Ln is the lanthanide cation). Neither of these features is found in the literature on known fluoride compounds”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“For example, the top two space groups in the ICSD are centrosymmetric Pnma and P21/c, accounting for approximately 16% of all structures, with ∼8% in each case. By contrast, Pnma is ranked 16th in Stable Structure database with 1.56% of the structures, while P21/c is found in only 0.7% of the structures, which is an order of magnitude lower than in the ICSD.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Space Group Analysis
What the sources say
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are a further 23,529 entries for compounds containing the highly radioactive elements Tc, Np, and Pu.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“TbHo2Tm9Co4 (mp-3170547) is an even more striking example among the intermetallics. This proposed alloy between three smaller rare earths and cobalt is predicted to be monoclinic and non-centrosymmetric, space group Pm, with a = 6.14 Å, b = 9.23 Å, c = 6.89 Å, and β = 90.1°.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not checked — no full text of the sourcecites [12] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
In the paper
“in TbSmSeO2 (mp-3169970), the Tb3+ and Sm3+ cations are predicted to be on separate crystallographic sites, even though their charges are the same and their 6-coordinate ionic radii are very similar (0.92 and 0.96 Å, respectively). Because of the ordering, the compound is proposed to be non-centrosymmetric in space group P3m1 with a ≈ 3.91 Å and c ≈ 6.92 Å.”
- Also stated in: § General Comments about the GNoME Explorer Database · hedged
“the disordered structure of (Tb/Sm)SeO2 would be isomorphous with the known centrosymmetric P3̅m1 structure of La2SeO2, which has a ≈ 4.09 Å and a ≈ 7.16 Å.9”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not checked — no full text of the sourcecites [9] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § General Comments about the GNoME Explorer Database · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, the top four space groups in the Stable Structure database are all non-centrosymmetric and account for ∼34% of all the structures. By contrast, in the ICSD there is only one non-centrosymmetric space group in the top 24 and it accounts for only 1% of all structures.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Space Group Analysis
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several of the proposed new structures have obvious analogues, which somewhat undermines the impact of their prediction.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“In the case of the proposed actinium (Ac3+) compounds, of which there are 27 (and approximately 6754 in the larger Stable Structure database), it should be noted that all isotopes of actinium are intensely radioactive with half-lives ranging from days to a few years.14”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not checked — no full text of the sourcecites [14] Kirby H. W., 2008 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AcEr3(B6)4 is predicted to be tetragonal with a ≈ 4.1 Å and c ≈ 16.5 Å, revealing that a proposed ordering of Ac3+ and Er3+ (which may or may not be experimentally viable) leads to a 4× superstructure along c and lowers the symmetry of the parent LaB6 structure from cubic to tetragonal.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are other examples of apparently novel and complex borides that are based upon the LaB6 structure, e.g., K2SmLuB24 (mp-3170680)”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The compositions are often not presented in a manner that an experimental materials chemist would find appropriate or helpful, nor are the usual rules of chemical nomenclature followed”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“K3Nd(AsO4)2 (mp-3196061) is a good example. It is predicted to be monoclinic, in space group P21, with a = 9.8 Å, b = 5.8 Å, c = 7.6 Å, and β = 91.87°. Since arsenates are often isostructural with phosphates, it is possible to recognize that the analogous phosphate, K3Nd(PO4)2, is known with a monoclinic P21/m structure (a = 9.5 Å, b = 5.6 Å, c = 7.4 Å, β = 90.95°).8”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not checked — no full text of the sourcecites [8] source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Tb16Ho(ErIr4)3 (mp-3170203) could be more usefully written as Tb16HoEr3Ir12. It is predicted to be tetragonal, P4̅ (non-centrosymmetric), with a = 10.92 Å and c = 6.38 Å. If we assume that very similar rare earths would be disordered, as discussed above, it can then be written as Ln20Ir12 or Ln5Ir3.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not checked — no full text of the sourcecites [13] source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Very similar rare earths would be disordered in the proposed structures.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The final step is to recognize that the pseudo-rhombohedral cell from the Stable Structure database with α = 59.7° is also nearly pseudocubic, thus accounting for the 4× discrepancy in the cell volume.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In only one case was the space group the same.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Strength:plain· not yet calibratedn = 10Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
In the paper
“A search in the ICSD for compounds containing Zr, Ir, and N then quickly led us to Zr4Ir2N,23 whose cubic unit cell is 4× larger than the cell of Hf4Ir8N4NbZr11 in the database.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
What the sources say
- Not checked — no full text of the sourcecites [23] source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, in addition to the hexaborides, there are many examples in the GNoME list that are clearly diborides, which is another important stoichiometry in the boride area.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For example, Cu8P4SrTm was identified as having the same structure as CaCu4P2,22 noting that the lattice parameters were switched between the two entries and that the predicted compound was pseudo-tetragonal.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
What the sources say
- Not checked — no full text of the sourcecites [22] source
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In fact, we have yet to find any strikingly novel compounds in the GNoME and Stable Structure listings, although we anticipate that there must be some among the 384,870 compositions.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The “discoveries” of new actinium compounds, such as AcPO3 (mp-3170055), are therefore impractical and not very novel.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The few known actinium compounds are isomorphous with their stable lanthanum analogues.
- Rests on an assumption: Ionic actinium compounds have no industrial applications.
In the paper
“Each of the 2047 entries in the GNoME Explorer compilation has been presented with chemical composition, space group, atomic coordinates, formation energy, and, where appropriate, tentative assignments of oxidation states.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > General Comments about the GNoME Explorer Database
What the sources say
- Not matchedcites [3] https://next-gen.materialsproject.org/materials/gnome (accessed November 29, 2023).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Almost 400,000 of the structures are deemed to be stable and have been listed in a Stable Structure database,2”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Introduction
What the sources say
- Not matchedcites [2] https://github.com/google-deepmind/materials_discovery (accessed January 27, 2024).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Predictions of ordering usually lead to lower symmetry structures and in some cases to superstructures, as illustrated above.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Space Group Analysis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, many compounds undergo phase transitions to lower symmetry structures on cooling to low temperatures, although we have no evidence of this from the GNoME entries that we have examined.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Space Group Analysis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is questionable whether Sr and Tm would order on distinct crystallographic sites. If Tm were to substitute as the expected Tm3+, this would require electron-doping a Cu1+ compound, which is unlikely.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since no functionality has been demonstrated for the 384,870 compositions in the Stable Structure database, they cannot yet be regarded as materials.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Chemical compounds become materials when they demonstrate some utility.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is not surprising to an experimental materials chemist, because it is quite rare to find an entirely new structure type in the inorganic world.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“This points to an interesting contraindication in materials design: soft anions such as Te2– and I– readily permit cation transport but do not possess the requisite electrochemical (redox) stability for use as solid electrolytes. Conversely, hard anions such as O2– and F– are redox-stable, but they bind cations strongly, and the resulting materials usually display limited ionic conductivity.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
What the sources say
- Not checked — no full text of the sourcecites [29] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in Hf4Ir8N4NbZr11 our starting point was to recognize that Hf and Zr are two of the most similar elements in the periodic table and are almost certain to be disordered on the same sites (pointing us toward Zr11Ir8N4Nb). Furthermore, in a metal-rich compound, Nb and Zr/Hf are also likely to alloy on the same crystallographic site.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Randomly Selected Examples from the Stable Structure Database
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Hf and Zr are almost certain to be disordered on the same sites, and Nb and Zr/Hf are likely to alloy on the same crystallographic site.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while many of the new compositions are trivial adaptations of known materials, the computational approach delivers credible overall compositions, which gives us confidence that the underlying approach is sound.”
§ Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery > Conclusions
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Chemical compounds become materials when they demonstrate some utility.
“it is the usual practice in the field that chemical compounds become materials when they demonstrate some utility.”
The authors state this as an assumption.
Very similar rare earths would be disordered in the proposed structures.
“If we assume that very similar rare earths would be disordered, as discussed above”
The paper relies on this without support.
Ac compounds would be isomorphous with their La analogues.
“However, we would expect Ac compounds would be isomorphous with their La analogues.”
The paper relies on this without support.
Tl1+ and Rb1+ are close in size and identical in their charge states.
“Tl1+ and Rb1+ are also close in size and identical in their charge states.”
The paper relies on this without support.
Hf and Zr are almost certain to be disordered on the same sites, and Nb and Zr/Hf are likely to alloy on the same crystallographic site.
“Hf and Zr are two of the most similar elements in the periodic table and are almost certain to be disordered on the same sites”
“Nb and Zr/Hf are also likely to alloy on the same crystallographic site.”
The paper relies on this without support.
The 14 rare-earth elements and yttrium have very similar chemistries, and zirconium and hafnium have virtually identical chemistries.
“the recognition that the 14 rare-earth elements and yttrium have very similar chemistries could be incorporated as well as the recognition that zirconium and hafnium, among other important metal pairs, have virtually identical chemistries.”
The paper relies on this without support.
The few known actinium compounds are isomorphous with their stable lanthanum analogues.
“the few that are known are isomorphous with their stable lanthanum analogues (e.g., AcPO4 and LaPO4).”
The paper relies on this without support.
Ionic actinium compounds have no industrial applications.
“ionic actinium compounds, which always contain Ac3+, have no industrial applications.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Known zirconium nitride has two different formulas
MinorThe text identifies the known comparison compound as Zr4Ir2N, but the Figure 1 caption identifies it as Zr4Ir4N.
“A search in the ICSD for compounds containing Zr, Ir, and N then quickly led us to Zr4Ir2N,23”
“Figure: Figure 1 Views of the crystal structures of (a) known Zr4Ir4N (ICSD 640826)”
Top four space-group percentages total about 35%, not 34%
MinorThe four percentages in Table 1 are 12.7, 10.2, 6.88, and 5.17. They sum to 34.95%, which rounds to 35%, rather than the stated approximately 34%.
Reported 34 · recomputed 34.95
“the top four space groups in the Stable Structure database are all non-centrosymmetric and account for ∼34% of all the structures.”
Two different values are assigned to the same lattice parameter
MinorThe description of La2SeO2 gives two different values for its parameter a. The second parameter may have been intended to be c, but it is printed as a.
“La2SeO2, which has a ≈ 4.09 Å”
“and a ≈ 7.16 Å.”
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Chemistry-transforming applications of artificial intelligence
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Хемоинформатика как самостоятельная научная дисциплина
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Metadata
- Authors
- Cheetham, A. K., & Seshadri, R.
- Affiliations
- University of California, Santa Barbara (US)
- Corresponding author
- Cheetham, Anthony K.
- Seshadri, Ram
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-04-08
- Volume
- 36
- Issue
- 8
- Pages
- 3490-3495
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- PMC
- PMC11044265
- OpenAlex
- W4394582003
- Cited by
- 191
- References
- 27
- Citations by year
- 2026: 89 · 2025: 77 · 2024: 22
- FWCI
- 16.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chemistry of Materials (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Basic Energy Sciences (DE-SC0024422)
- Ras Al Khaimah Center for Advanced Materials
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Crystal Structures and Properties
- Keywords
- materials discovery, deep learning, compound prediction, high-throughput density functional theory, structural stability, machine learning, domain knowledge, crystallography, materials synthesis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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