Scaling deep learning for materials discovery — TEI-tagged view

Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin D. Cubuk · 2023 · materials-science · doi:10.1038/s41586-023-06735-9 · TEI xml · tagged edition · index · concepts · authors
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Other fields define these differently the same word, defined another way in another discipline
DFT 4 fields · active learning 3 fields
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convex hull decomposition energy hit rate3 active learning4 GNoME Materials Project r2SCAN experimental match prototypes2 graph networks trained at scale efficiency of materials discovery human chemical intuition learned interatomic potentials condensed-phase molecular-dynamics simulations zero-shot prediction of ionic conductivity large-scale active learning first models that accurately predict stability diverse candidate structures symmetry-aware partial substitutions (SAPS) random structure search graph neural networks (GNNs) material properties candidate structures filtered candidates DFT12 data flywheel more robust models larger datasets model-based filtration and DFT predictions2 structural candidates ionic substitution probabilities incomplete replacements volume-based test-time augmentation uncertainty quantification through deep ensembles polymorphs2 compositional models reduced chemical formulas oxidation-state balancing relaxed constraints ab initio random structure searching (AIRSS) relative stability (decomposition energy) Vienna Ab initio Simulation Package (VASP) number of stable materials discovered total energy of a crystal one-hot embedding of the elements message-passing formulation shallow multilayer perceptrons (MLPs) swish nonlinearities average adjacency of atoms mean absolute error (MAE) test loss performance power law with further data neural scaling laws out-of-distribution tasks high-energy local minima universal energy predictor ionic relaxations first-principles energies and forces general-purpose machine-learning interatomic potential (MLIP) bulk solids ab initio molecular dynamics (AIMD) general applicability and adoption large-scale pretraining of MLIPs unprecedented zero-shot accuracy superionic conductors material-specific data increasing the pretraining dataset power-law improvements in accuracy pretrained GNoME potential unseen compositions melted structures structures including vacancies transferability of MLIPs potential pretrained on GNoME data potential trained from scratch GNoME-pretrained randomly initialized potential distribution shift2 stable crystals identically distributed data at train and test times out-of-distribution effort emergent out-of-distribution capabilities unseen chemical spaces kinetic properties system-or-tool-building173
Method, as described: rounds of active learning: generate candidates, filter with graph networks, verify the survivors with DFT, retrain on the result

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonemergent out-of-distribution generalisation in a graph network trained at scale— “Moreover, these networks develop emergent out-of-distribution generalization. For example, GNoME enables accurate predictions of structures with 5+ unique elements (despite omission from training), providing one of the first strategies to efficiently explore this chemical space.”
claim-typeexistence— “In particular, GNoME models found 2.2 million crystal structures stable with respect to the Materials Project. Of these, 381,000 entries live on the updated convex hull as newly discovered materials.”
method — evidence— “The energy of the filtered candidates is computed using DFT, both verifying model predictions and serving as a data flywheel to train more robust models on larger datasets in the next round of active learning.”
object of study — evidence— “In particular, GNoME models found 2.2 million crystal structures stable with respect to the Materials Project. Of these, 381,000 entries live on the updated convex hull as newly discovered materials.”
research question — evidence— “Although data-driven methods that aid in further materials discovery have been pursued, thus far, machine-learning techniques have been ineffective in estimating stability (decomposition energy) with respect to the convex hull of energies from competing phases.”
contribution — evidence— “In particular, GNoME models found 2.2 million crystal structures stable with respect to the Materials Project. Of these, 381,000 entries live on the updated convex hull as newly discovered materials.”
transferfrom machine learning: neural scaling laws, carried from language and vision into materials-stability prediction— “Consistent with observations in other domains of machine learning, we observe that our neural networks predictions improve as a power law with the amount of data.”transfers[0]
mechanismthe data flywheel DFT verifies the model's filtered candidates and the verified results become the next round's training data, so the same computation both audits the model and improves it— “The energy of the filtered candidates is computed using DFT, both verifying model predictions and serving as a data flywheel to train more robust models on larger datasets in the next round of active learning.”mechanisms[0]
mechanismpower-law scaling of prediction error prediction quality improves as a power law in the amount of data, matching what has been observed in language and vision rather than being specific to materials— “Consistent with observations in other domains of machine learning, we observe that our neural networks predictions improve as a power law with the amount of data.”mechanisms[1]
contradicts the prior position that machine learning cannot estimate stability against the convex hull— “Although data-driven methods that aid in further materials discovery have been pursued, thus far, machine-learning techniques have been ineffective in estimating stability (decomposition energy) with respect to the convex hull of energies from competing phases.”claim_relations[0]
extends the Materials Project, OQMD, AFLOWLIB and NOMAD catalogues of computationally stable structures— “Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to our own recalculations (see Methods).”claim_relations[1]
sense'stable' sense 1 = below the convex hull OF A PRIOR CATALOGUE - 2.2 million structures— “In particular, GNoME models found 2.2 million crystal structures stable with respect to the Materials Project. Of these, 381,000 entries live on the updated convex hull as newly discovered materials.”senses[0]
sense'stable' sense 2 = ON the UPDATED convex hull after this work - 381,000 entries, six times fewer— “In particular, GNoME models found 2.2 million crystal structures stable with respect to the Materials Project. Of these, 381,000 entries live on the updated convex hull as newly discovered materials.”senses[1]
sensestability is REVOCABLE: these materials can be bumped off the hull by future discoveries, exactly as this work bumps off 5,000 others— “Consistent with other literature on structure prediction, the GNoME materials could be bumped off the convex hull by future discoveries, similar to how GNoME displaces at least 5,000 ‘stable’ materials from the Materials Project and the OQMD.”senses[2]
sense'meaningfully stable' = distributed like known stable phases, not merely filling gaps in the hull— “The similarities in distribution suggest that the found materials are meaningfully stable with respect to competing phases and not just ‘filling in the convex hull.’”senses[3]
sense'experimental match' = integer-rounded composition match AND a pymatgen structure match, declared a lower bound— “Given that we are enforcing a strict compositional match, our matching process is still relatively conservative and is likely to yield a lower bound.”senses[4]
senseGNoME discovery flywheel - generate candidates, filter with GNNs, verify energies with DFT, and reuse results as next-round training data— “In this paper, we scale up machine learning for materials exploration through large-scale active learning, yielding the first models that accurately predict stability and, therefore, can guide materials discovery. Our approach relies on two pillars: first, we establish methods for generating diverse candidate structures, including new…senses[5]
senseStructural framework - modify known crystals, expand substitutions with SAPS, quantify uncertainty, cluster polymorphs, and rank for DFT— “To generate and filter candidates, we use two frameworks, which are visualized in Fig. 1a. First, structural candidates are generated by modifications of available crystals. However, we strongly augment the set of substitutions by adjusting ionic substitution probabilities to give priority to discovery and use newly proposed symmetry aware…senses[6]
senseCompositional framework - predict from reduced formulas, relax oxidation constraints, filter compositions, and initialize AIRSS structures— “To generate and filter candidates, we use two frameworks, which are visualized in Fig. 1a. First, structural candidates are generated by modifications of available crystals. However, we strongly augment the set of substitutions by adjusting ionic substitution probabilities to give priority to discovery and use newly proposed symmetry aware…senses[7]
senseGNoME graph model - element embeddings and message passing predict total crystal energy— “All GNoME models are GNNs that predict the total energy of a crystal. Inputs are converted to a graph through a one-hot embedding of the elements. We follow the message-passing formulation, in which aggregate projections are shallow multilayer perceptrons (MLPs) with swish nonlinearities. For structural models, we find it important to…senses[8]
senseScaling law - GNoME test loss improves by a power law as training data increase— “The test loss performance of GNoME models exhibit improvement as a power law with further data. These results are in line with neural scaling laws in deep learning and suggest that further discovery efforts could continue to improve generalization. Emphatically, unlike the case of language or vision, in materials science, we can continue to…senses[9]
senseUniversal energy predictor - model handling diverse material structures including random-search inputs outside the training distribution— “The test loss performance of GNoME models exhibit improvement as a power law with further data. These results are in line with neural scaling laws in deep learning and suggest that further discovery efforts could continue to improve generalization. Emphatically, unlike the case of language or vision, in materials science, we can continue to…senses[10]
senseGeneral-purpose MLIP - potential pretrained on diverse ionic-relaxation energies and forces for zero-shot bulk-solid use— “The process of discovery of stable crystals also provides a data source beyond stable materials. In particular, the ionic relaxations involve computation of first-principles energies and forces for a diverse set of materials structures. This generates a dataset of unprecedented diversity and scale, which we explore to pretrain a…senses[11]
senseZero-shot evaluation - pretrained potential applied to unseen compositions, melted structures, vacancies, and AIMD data without AIMD training— “We scale pretraining of a NequIP potential on data sampled from ionic relaxations. Increasing the pretraining dataset, we observe consistent power-law…senses[12]
controlrecomputation under the more accurate r2SCAN functional, to test whether discovery overfitted the cheaper PBE one — partial — rules out a discovery set that exists only under the cheap functional it was searched with
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controls[0]
controlan explicit check that model-based exploration did not overfit the simulation parameters — held
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— rules out model-based exploration finding materials that are artefacts of the simulation parameters it was trained against
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controls[1]
controlmatching against crystals experimentally realised by other groups after the training snapshot — held
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— rules out a match rate inflated by structures the model could have seen, since these were realised experimentally after the training snapshot
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controls[2]
controlcomparison against compositions added to the Materials Project after the March 2021 snapshot — held
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— rules out a match rate computed only against the database the model was trained on
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controls[3]
controla sensitivity check on the parser and matcher tolerances used to count experimental matches — partial
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— rules out an experimental match count that exists only at one parser and matcher tolerance setting
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controls[4]
notethe prior position that machine learning cannot estimate stability against the convex hull — the paper states that position as the state of the art it overturns, and the hit-rate figures are what it offers against it
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claim_relations[0]
weightthe prior position that machine learning cannot estimate stability against the convex hull — 0.95
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notethe Materials Project, OQMD, AFLOWLIB and NOMAD catalogues of computationally stable structures — extends by an order of magnitude AND revises downward - at least 5,000 of their entries are displaced
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claim_relations[1]
weightthe Materials Project, OQMD, AFLOWLIB and NOMAD catalogues of computationally stable structures — 0.95
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claim_relations[1]
auditGNoME —contrasted-with→ Materials Project — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says GNoME displaces some 'stable' materials from the Materials Project; contrasted-with is loose.
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differs inGNoME —contrasted-with→ Materials Project — scale, and the direction of revision - GNoME removes at least 5,000 entries the Materials Project called stable
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weightGNoME —contrasted-with→ Materials Project — 0.95
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effect direction (stated by the tagger, no quotation)active learning —causes→ hit rate — increasesconcept_edges[1]
weightactive learning —causes→ hit rate — 0.85
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effect directiongraph networks trained at scale —causes→ efficiency of materials discovery — increases
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notegraph networks trained at scale —causes→ efficiency of materials discovery — Scaled graph networks improve discovery efficiency by an order of magnitude.
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effect directionlarge-scale active learning —causes→ first models that accurately predict stability — increases
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notelarge-scale active learning —causes→ first models that accurately predict stability — The paper says large-scale active learning yields the first accurate stability models.
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auditmodel-based filtration and DFT —causes→ predictions — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but filtration and DFT generate candidates; causes 'predictions' is loose.
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effect directionmodel-based filtration and DFT —causes→ predictions — increases
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notemodel-based filtration and DFT —causes→ predictions — The combined filtration/DFT flywheel is stated to improve predictions.
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effect directionincreasing the pretraining dataset —causes→ power-law improvements in accuracy — increases
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noteincreasing the pretraining dataset —causes→ power-law improvements in accuracy — Increasing pretraining scale produces consistent power-law accuracy improvements.
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noteGNoME-pretrained —contrasted-with→ randomly initialized potential — The figure compares pretrained and randomly initialized potentials under distribution shift.
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effect directionpotential pretrained on GNoME data —causes→ transferability of MLIPs — increases
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notepotential pretrained on GNoME data —causes→ transferability of MLIPs — The pretrained potential shows systematic, strong transferability improvements over training from scratch.
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auditgraph neural networks (GNNs) —causes→ stable crystals — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'GNNs'; endpoint abbreviated.
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effect directiongraph neural networks (GNNs) —causes→ stable crystals — increases
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notegraph neural networks (GNNs) —causes→ stable crystals — Large, diverse first-principles training increases stable-crystal discovery by more than an order of magnitude.
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outcomecontrol — partial
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controls[0]
outcomecontrol — held
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outcomecontrol — held
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outcomecontrol — held
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controls[3]
outcomecontrol — partial
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controls[4]
no type for thisgraph neural networks (GNNs) → candidate structures — GNoME graph networks are used to filter candidate structures. Used-to-filter is absent from the eight types.
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unhoused_relations[0]
no type for thisDFT → model predictions — DFT verifies the model predictions. Verifies is absent from the eight types.
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unhoused_relations[1]
no type for thisGNoME dataset → general-purpose machine-learning interatomic potential (MLIP) — The GNoME dataset enables pretraining a general-purpose MLIP. Enablement is absent from the eight types.
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unhoused_relations[2]
no type for thisfinal GNoME models → universal energy predictor — Final GNoME models are a substantial step toward a universal energy predictor. Progress-toward is absent from the eight types.
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unhoused_relations[3]
limitationthe discovered materials can themselves be bumped off the convex hull by future work - stability is a claim about a catalogue at a moment
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limitations[0]
limitationthe experimental-match count is a lower bound, because the compositional match is enforced strictly
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limitations[1]
limitationthe matching answer moves by about 100 structures depending on parser and matcher tolerance settings
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limitations[2]
limitationthe discovery functional was chosen for a speed-accuracy trade-off rather than accuracy, and 84% rather than 100% survive the more accurate one
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limitations[3]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingGNoME found 2.2 million structures stable relative to the Materials Project, of which 381,000 lie on the updated convex hull.
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content_tags.findings[0]
findingthis work removes at least 5,000 entries that two prior catalogues had called stable
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content_tags.findings[1]
findingthe networks generalise to structures with 5+ unique elements although such structures were omitted from training
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content_tags.findings[2]
findingthe discovered materials are distributed like known stable phases rather than merely filling gaps in the hull
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content_tags.findings[3]
quantity2.2 million structures stable with respect to the Materials Project; 381,000 on the updated hull —
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content_tags.quantities[0]
quantityat least 5,000 previously 'stable' materials displaced from the Materials Project and OQMD —
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content_tags.quantities[1]
quantityenergies predicted to 11 meV per atom; hit rate above 80% with structure, 33% per 100 trials with composition, against 1% before —
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content_tags.quantities[2]
quantity736 of the ICSD's experimental structures match GNoME discoveries; 184 are novel since the project began —
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content_tags.quantities[3]
quantity4,235 composition matches narrowed to 4,180 parsed and then to 736 —
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content_tags.quantities[4]
quantity84% of binaries and ternaries and 86.8% of quaternaries survive r2SCAN —
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content_tags.quantities[5]
quantity45,500 novel prototypes, a 5.6 times increase on the Materials Project's 8,000 —
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content_tags.quantities[6]
quantitylayered materials rise from about 1,000 to about 52,000 —
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content_tags.quantities[7]
quantitythe ICSD holds 20,000 computationally stable structures out of 200,000 entries —
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content_tags.quantities[8]
variabledecomposition energy
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content_tags.variables[0]
variablehit rate
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content_tags.variables[1]
definitionconvex hull
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content_tags.definitions[0]
definitionhit rate
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content_tags.definitions[1]
definitionexperimental match
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content_tags.definitions[2]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptconvex hull — not defined in this paper's own words; the surface of lowest-energy competing phases; being on it is what 'stable' means here
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concepts[0]
conceptdecomposition energy — not defined in this paper's own words; named in the paper as the thing 'stability' operationally is
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concepts[1]
concepthit rate — not defined in this paper's own words; the precision of stable predictions - above 80% with structure, 33% per 100 trials with composition
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[2]
conceptactive learning — not defined in this paper's own words; rounds in which DFT both verifies the model and supplies its next training set
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[3]
conceptGNoME — not defined in this paper's own words; graph networks for materials exploration, the system this paper announces
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[4]
conceptMaterials Project — not defined in this paper's own words; the catalogue stability is measured against - and the one this work displaces entries from
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[5]
conceptr2SCAN — not defined in this paper's own words; the more accurate functional used to check that discovery did not overfit the cheaper one
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[6]
conceptexperimental match — not defined in this paper's own words; a composition match rounded to integer formulas, then a pymatgen structure match
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[7]
conceptprototypes — not defined in this paper's own words; structure types; 45,500 novel ones, a 5.6x increase, used as the diversity argument
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[8]
conceptgraph networks trained at scale — not defined in this paper's own words; scaled models that generalize and accelerate materials discovery
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[9]
conceptefficiency of materials discovery — not defined in this paper's own words; discovery efficiency improved by an order of magnitude
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[10]
concepthuman chemical intuition — not defined in this paper's own words; prior search guidance escaped by many predicted structures
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[11]
conceptlearned interatomic potentials — not defined in this paper's own words; downstream models trained from first-principles trajectories
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[12]
conceptcondensed-phase molecular-dynamics simulations — not defined in this paper's own words; downstream simulation use of learned potentials
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[13]
conceptzero-shot prediction of ionic conductivity — not defined in this paper's own words; downstream property prediction without task-specific training
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[14]
conceptlarge-scale active learning — not defined in this paper's own words; iterative scaling strategy for materials exploration
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[15]
conceptfirst models that accurately predict stability — not defined in this paper's own words; models yielded by large-scale active learning
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[16]
conceptdiverse candidate structures — not defined in this paper's own words; candidate set generated by the first pipeline pillar
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[17]
conceptsymmetry-aware partial substitutions (SAPS) — not defined in this paper's own words; candidate-generation method for incomplete substitutions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[18]
conceptrandom structure search — not defined in this paper's own words; second diverse candidate-generation method
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[19]
conceptgraph neural networks (GNNs) — not defined in this paper's own words; structure- or composition-conditioned property models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[20]
conceptmaterial properties — not defined in this paper's own words; quantities modelled from structure or composition
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[21]
conceptcandidate structures — not defined in this paper's own words; generated structures filtered by GNoME
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[22]
conceptfiltered candidates — not defined in this paper's own words; candidates evaluated by density-functional theory
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[23]
conceptDFT — not defined in this paper's own words; first-principles evaluator verifying model predictions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[24]
conceptdata flywheel — not defined in this paper's own words; DFT feedback that supplies the next active-learning round
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[25]
conceptmore robust models — not defined in this paper's own words; models trained on larger feedback datasets
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[26]
conceptlarger datasets — not defined in this paper's own words; data accumulated for later active-learning rounds
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[27]
conceptmodel-based filtration and DFT — not defined in this paper's own words; combined flywheel that improves predictions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[28]
conceptpredictions — not defined in this paper's own words; model outputs improved by the discovery flywheel
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[29]
conceptstructural candidates — not defined in this paper's own words; crystal modifications used in the structural framework
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[30]
conceptionic substitution probabilities — not defined in this paper's own words; probabilities adjusted to prioritize discovery
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[31]
conceptincomplete replacements — not defined in this paper's own words; partial substitutions enabled by SAPS
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[32]
conceptvolume-based test-time augmentation — not defined in this paper's own words; GNoME filtration technique
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[33]
conceptuncertainty quantification through deep ensembles — not defined in this paper's own words; second GNoME filtration technique
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[34]
conceptpolymorphs — not defined in this paper's own words; clustered structures ranked for DFT evaluation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[35]
conceptcompositional models — not defined in this paper's own words; models predicting stability without structural information
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[36]
conceptreduced chemical formulas — not defined in this paper's own words; inputs to composition-only models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[37]
conceptoxidation-state balancing — not defined in this paper's own words; composition-generation method described as overly strict
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[38]
conceptrelaxed constraints — not defined in this paper's own words; looser constraints used for composition filtration
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[39]
conceptab initio random structure searching (AIRSS) — not defined in this paper's own words; random structural evaluation of filtered compositions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[40]
conceptrelative stability (decomposition energy) — not defined in this paper's own words; thresholding quantity relative to competing phases
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[41]
conceptVienna Ab initio Simulation Package (VASP) — not defined in this paper's own words; software used for DFT evaluation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[42]
conceptnumber of stable materials discovered — not defined in this paper's own words; discovery-output measure
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[43]
concepttotal energy of a crystal — not defined in this paper's own words; prediction target of all GNoME models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[44]
conceptone-hot embedding of the elements — not defined in this paper's own words; conversion from crystal inputs to graph features
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[45]
conceptmessage-passing formulation — not defined in this paper's own words; GNoME graph-network update formulation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[46]
conceptshallow multilayer perceptrons (MLPs) — not defined in this paper's own words; aggregate projections used during message passing
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[47]
conceptswish nonlinearities — not defined in this paper's own words; nonlinearity used by the MLPs
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[48]
conceptaverage adjacency of atoms — not defined in this paper's own words; dataset-wide normalizer for edge-to-node messages
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[49]
conceptmean absolute error (MAE) — not defined in this paper's own words; energy-prediction error benchmark
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[50]
concepttest loss performance — not defined in this paper's own words; quantity improving by a power law with more data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[51]
conceptpower law with further data — not defined in this paper's own words; reported scaling relationship for GNoME test loss
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[52]
conceptneural scaling laws — not defined in this paper's own words; deep-learning scaling regularity matched by GNoME
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[53]
conceptout-of-distribution tasks — not defined in this paper's own words; random-search inputs used to test emergent generalization
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[54]
concepthigh-energy local minima — not defined in this paper's own words; random-search examples outside structural-pipeline distribution
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[55]
conceptuniversal energy predictor — not defined in this paper's own words; community model toward which scaled GNoME is a step
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[56]
conceptionic relaxations — not defined in this paper's own words; source of diverse first-principles energies and forces
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[57]
conceptfirst-principles energies and forces — not defined in this paper's own words; reference quantities generated during relaxation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[58]
conceptgeneral-purpose machine-learning interatomic potential (MLIP) — not defined in this paper's own words; bulk-solids potential pretrained from the GNoME dataset
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[59]
conceptbulk solids — not defined in this paper's own words; material class targeted by the general-purpose potential
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[60]
conceptab initio molecular dynamics (AIMD) — not defined in this paper's own words; typical per-material data source for prior potentials
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[61]
conceptgeneral applicability and adoption — not defined in this paper's own words; properties limited by per-material training
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[62]
conceptlarge-scale pretraining of MLIPs — not defined in this paper's own words; pretraining that yields zero-shot models
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[63]
conceptunprecedented zero-shot accuracy — not defined in this paper's own words; performance enabled by large-scale pretraining
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[64]
conceptsuperionic conductors — not defined in this paper's own words; materials discoverable by the pretrained potentials
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[65]
conceptmaterial-specific data — not defined in this paper's own words; training data not required for zero-shot discovery
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[66]
conceptincreasing the pretraining dataset — not defined in this paper's own words; scaling intervention producing power-law accuracy gains
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[67]
conceptpower-law improvements in accuracy — not defined in this paper's own words; accuracy trend under increasing pretraining scale
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[68]
conceptpretrained GNoME potential — not defined in this paper's own words; potential evaluated zero-shot on an AIMD distribution
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[69]
conceptunseen compositions — not defined in this paper's own words; zero-shot inputs absent from training
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[70]
conceptmelted structures — not defined in this paper's own words; second unseen zero-shot structure class
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[71]
conceptstructures including vacancies — not defined in this paper's own words; third unseen zero-shot structure class
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[72]
concepttransferability of MLIPs — not defined in this paper's own words; distribution-shift capability improved by pretraining
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[73]
conceptpotential pretrained on GNoME data — not defined in this paper's own words; fine-tuned potential compared with training from scratch
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[74]
conceptpotential trained from scratch — not defined in this paper's own words; comparison baseline for transferability
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[75]
conceptGNoME-pretrained — not defined in this paper's own words; pretrained potential in the figure comparison
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[76]
conceptrandomly initialized potential — not defined in this paper's own words; non-pretrained comparison potential
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[77]
conceptdistribution shift — not defined in this paper's own words; 400 K to 1,000 K robustness test
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[78]
conceptstable crystals — not defined in this paper's own words; discovery count increased by scaled GNNs
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[79]
conceptidentically distributed data at train and test times — not defined in this paper's own words; standard learning assumption challenged by discovery
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[80]
conceptout-of-distribution effort — not defined in this paper's own words; character assigned to scientific discovery
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[81]
conceptemergent out-of-distribution capabilities — not defined in this paper's own words; capabilities demonstrated at GNoME scale
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[82]
conceptunseen chemical spaces — not defined in this paper's own words; novel compositional regions reached by GNoME
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[83]
conceptkinetic properties — not defined in this paper's own words; new downstream prediction task
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[84]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
power-law improvement of test loss with data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[0]
precision of stable predictions, reported as a hit rate
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typemethod — a second reading DIFFERING from the recorded value; recorded: existence
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.claim_type
methodsystem-or-tool-building — a second reading DIFFERING from the recorded value; recorded: system-or-tool-building
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.method
phenomenondiscovery of energetically stable inorganic crystal structures at a scale experiment cannot reach — a second reading DIFFERING from the recorded value; recorded: emergent out-of-distribution generalisation in a graph network trained at scale
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.phenomenon

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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