Scaling deep learning for materials discovery

Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin D. Cubuk · Nature · 2023 · materials-science · 10.1038/s41586-023-06735-9 · record merchant2023-scaling-deep-learning-for-material
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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

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 85 concept tags on this paper, all 85 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
convex hull decomposition energy hit rate active learning GNoME Materials Project r2SCAN experimental match prototypes 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 DFT data flywheel more robust models larger datasets model-based filtration and DFT predictions structural candidates ionic substitution probabilities incomplete replacements volume-based test-time augmentation uncertainty quantification through deep ensembles polymorphs 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 shift stable crystals identically distributed data at train and test times out-of-distribution effort emergent out-of-distribution capabilities unseen chemical spaces kinetic properties the data flywheel power-law scaling of prediction error power-law improvement of test loss with data precision of stable predictions, reported as a hit rate system-or-tool-building
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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relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field
unrecordable relationunhoused_relationsa relation the paper asserts that this vocabulary cannot express, with the reason

The paper

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Every tag on this page

alternatives 3 · claim_relations 2 · claim_type 1 · concept_edges 9 · concepts 85 · content_tags 20 · contribution 1 · controls 5 · limitations 4 · mechanisms 2 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 13 · transfers 1 · unhoused_relations 4

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 7 of 9 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studyinorganic crystal structures and whether a graph neural network can predict which are thermodynamically stable
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object_of_studythe paper's words
research_questionwhether machine learning can estimate stability against the convex hull accurately enough to guide discovery, which prior methods could not
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research_questionthe paper's words
contribution2.2 million structures stable with respect to the Materials Project, of which 381,000 sit on the updated convex hull, plus learned interatomic potentials from the resulting dataset
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contributionthe paper's words
claim_typeexistence
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claim_typethe paper's words
phenomenonemergent out-of-distribution generalisation in a graph network trained at scale
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phenomenonthe paper's words
methodsystem-or-tool-building
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methodthe paper's words
mechanismsDFT 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
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mechanisms[0]0.95the tagger's words
mechanismsprediction 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
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mechanisms[1]0.95the tagger's words
limitationsthe 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]0.95the tagger's words
limitationsthe experimental-match count is a lower bound, because the compositional match is enforced strictly
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limitations[1]0.95the tagger's words
limitationsthe matching answer moves by about 100 structures depending on parser and matcher tolerance settings
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limitations[2]0.95the tagger's words
limitationsthe 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]0.95the tagger's words
transfersneural scaling laws, carried from language and vision into materials-stability prediction
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transfers[0]0.95the tagger's words
controlsrecomputation under the more accurate r2SCAN functional, to test whether discovery overfitted the cheaper PBE one
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eliminates: a discovery set that exists only under the cheap functional it was searched with · outcome: partial
controls[0]0.95the tagger's words
controlsan explicit check that model-based exploration did not overfit the simulation parameters
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eliminates: model-based exploration finding materials that are artefacts of the simulation parameters it was trained against · outcome: held
controls[1]0.95the tagger's words
controlsmatching against crystals experimentally realised by other groups after the training snapshot
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eliminates: a match rate inflated by structures the model could have seen, since these were realised experimentally after the training snapshot · outcome: held
controls[2]0.95the tagger's words
controlscomparison against compositions added to the Materials Project after the March 2021 snapshot
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eliminates: a match rate computed only against the database the model was trained on · outcome: held
controls[3]0.95the tagger's words
controlsa sensitivity check on the parser and matcher tolerances used to count experimental matches
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eliminates: an experimental match count that exists only at one parser and matcher tolerance setting · outcome: partial
controls[4]0.95the tagger's words
conceptsconvex hull
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also in: convex hull
concepts[0]0.95the paper's words
conceptsdecomposition energy
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concepts[1]0.95the paper's words
conceptshit rate
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also in: hit rate
concepts[2]0.95the paper's words
conceptsactive learning
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concepts[3]0.95the paper's words
conceptsGNoME
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also in: GNoME
concepts[4]0.95the paper's words
conceptsMaterials Project
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conceptsr2SCAN
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also in: r2SCAN
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conceptsexperimental match
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conceptsprototypes
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also in: prototypes
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conceptsgraph networks trained at scale
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concepts[9]0.95the paper's words
conceptsefficiency of materials discovery
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conceptshuman chemical intuition
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conceptslearned interatomic potentials
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conceptscondensed-phase molecular-dynamics simulations
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conceptszero-shot prediction of ionic conductivity
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conceptslarge-scale active learning
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conceptsfirst models that accurately predict stability
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concepts[16]0.95the paper's words
conceptsdiverse candidate structures
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conceptssymmetry-aware partial substitutions (SAPS)
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conceptsrandom structure search
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conceptsgraph neural networks (GNNs)
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conceptsmaterial properties
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conceptscandidate structures
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conceptsfiltered candidates
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conceptsDFT
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also in: DFT
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conceptsdata flywheel
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conceptsmore robust models
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conceptslarger datasets
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conceptsmodel-based filtration and DFT
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conceptspredictions
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also in: predictions
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conceptsstructural candidates
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conceptsionic substitution probabilities
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conceptsincomplete replacements
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conceptsvolume-based test-time augmentation
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conceptsuncertainty quantification through deep ensembles
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conceptspolymorphs
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also in: polymorphs
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conceptscompositional models
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conceptsreduced chemical formulas
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conceptsoxidation-state balancing
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conceptsrelaxed constraints
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conceptsab initio random structure searching (AIRSS)
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conceptsrelative stability (decomposition energy)
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conceptsVienna Ab initio Simulation Package (VASP)
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conceptsnumber of stable materials discovered
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conceptstotal energy of a crystal
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conceptsone-hot embedding of the elements
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conceptsmessage-passing formulation
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conceptsshallow multilayer perceptrons (MLPs)
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conceptsswish nonlinearities
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conceptsaverage adjacency of atoms
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conceptsmean absolute error (MAE)
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conceptstest loss performance
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conceptspower law with further data
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conceptsneural scaling laws
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conceptsout-of-distribution tasks
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conceptshigh-energy local minima
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conceptsuniversal energy predictor
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conceptsionic relaxations
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conceptsfirst-principles energies and forces
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conceptsgeneral-purpose machine-learning interatomic potential (MLIP)
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conceptsbulk solids
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also in: bulk solids
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conceptsab initio molecular dynamics (AIMD)
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conceptsgeneral applicability and adoption
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conceptslarge-scale pretraining of MLIPs
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conceptsunprecedented zero-shot accuracy
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conceptssuperionic conductors
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conceptsmaterial-specific data
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conceptsincreasing the pretraining dataset
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conceptspower-law improvements in accuracy
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conceptspretrained GNoME potential
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conceptsunseen compositions
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conceptsmelted structures
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conceptsstructures including vacancies
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conceptstransferability of MLIPs
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conceptspotential pretrained on GNoME data
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conceptspotential trained from scratch
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conceptsGNoME-pretrained
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conceptsrandomly initialized potential
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conceptsdistribution shift
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conceptsstable crystals
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conceptsidentically distributed data at train and test times
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conceptsout-of-distribution effort
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conceptsemergent out-of-distribution capabilities
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conceptsunseen chemical spaces
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conceptskinetic properties
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unhoused_relationsgraph neural networks (GNNs) → candidate structures
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GNoME graph networks are used to filter candidate structures. Used-to-filter is absent from the eight types.
unhoused_relations[0]0.95the tagger's words
unhoused_relationsDFT → model predictions
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DFT verifies the model predictions. Verifies is absent from the eight types.
unhoused_relations[1]0.95the tagger's words
unhoused_relationsGNoME dataset → general-purpose machine-learning interatomic potential (MLIP)
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The GNoME dataset enables pretraining a general-purpose MLIP. Enablement is absent from the eight types.
unhoused_relations[2]0.95the tagger's words
unhoused_relationsfinal GNoME models → universal energy predictor
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Final GNoME models are a substantial step toward a universal energy predictor. Progress-toward is absent from the eight types.
unhoused_relations[3]0.95the tagger's words
senses'stable' sense 1 = below the convex hull OF A PRIOR CATALOGUE - 2.2 million structures
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senses[0]0.95the tagger's words
senses'stable' sense 2 = ON the UPDATED convex hull after this work - 381,000 entries, six times fewer
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senses[1]0.95the tagger's words
sensesstability is REVOCABLE: these materials can be bumped off the hull by future discoveries, exactly as this work bumps off 5,000 others
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senses[2]0.95the tagger's words
senses'meaningfully stable' = distributed like known stable phases, not merely filling gaps in the hull
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senses[3]0.95the tagger's words
senses'experimental match' = integer-rounded composition match AND a pymatgen structure match, declared a lower bound
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senses[4]0.95the tagger's words
sensesGNoME discovery flywheel - generate candidates, filter with GNNs, verify energies with DFT, and reuse results as next-round training data
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senses[5]0.95the tagger's words
sensesStructural framework - modify known crystals, expand substitutions with SAPS, quantify uncertainty, cluster polymorphs, and rank for DFT
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senses[6]0.95the tagger's words
sensesCompositional framework - predict from reduced formulas, relax oxidation constraints, filter compositions, and initialize AIRSS structures
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senses[7]0.95the tagger's words
sensesGNoME graph model - element embeddings and message passing predict total crystal energy
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[8]0.95the tagger's words
sensesScaling law - GNoME test loss improves by a power law as training data increase
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[9]0.95the tagger's words
sensesUniversal energy predictor - model handling diverse material structures including random-search inputs outside the training distribution
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
senses[10]0.95the tagger's words
sensesGeneral-purpose MLIP - potential pretrained on diverse ionic-relaxation energies and forces for zero-shot bulk-solid use
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senses[11]0.95the tagger's words
sensesZero-shot evaluation - pretrained potential applied to unseen compositions, melted structures, vacancies, and AIMD data without AIMD training
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senses[12]0.95the tagger's words
alternativesclaim_type: method
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a second reading DIFFERING from the recorded value. Recorded: existence — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is).,
alternatives.claim_type0.7the tagger's words
alternativesmethod: system-or-tool-building
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
a second reading DIFFERING from the recorded value. Recorded: system-or-tool-building — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is).,
alternatives.method0.8the tagger's words
alternativesphenomenon: discovery of energetically stable inorganic crystal structures at a scale experiment cannot reach
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
a second reading DIFFERING from the recorded value. Recorded: emergent out-of-distribution generalisation in a graph network trained at scale — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is).,
alternatives.phenomenon0.85the tagger's words
claim_relationscontradicts → the prior position that machine learning cannot estimate stability against the convex hull
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note: the paper states that position as the state of the art it overturns, and the hit-rate figures are what it offers against it
claim_relations[0]the tagger's words
claim_relationsextends → the Materials Project, OQMD, AFLOWLIB and NOMAD catalogues of computationally stable structures
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note: extends by an order of magnitude AND revises downward - at least 5,000 of their entries are displaced
claim_relations[1]the tagger's words
concept_edgesGNoME —contrasted-with→ Materials Project
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differs in: scale, and the direction of revision - GNoME removes at least 5,000 entries the Materials Project called stable · strength of the relation: 0.95 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.
concept_edges[0]the tagger's words
concept_edgesactive learning —causes→ hit rate
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direction on the measure: increases · strength of the relation: 0.85 · direction on the measure (stated by the tagger, no quotation): increases
concept_edges[1]the tagger's words
concept_edgesgraph networks trained at scale —causes→ efficiency of materials discovery
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direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: Scaled graph networks improve discovery efficiency by an order of magnitude.
concept_edges[3]0.95the tagger's words
concept_edgeslarge-scale active learning —causes→ first models that accurately predict stability
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: The paper says large-scale active learning yields the first accurate stability models.
concept_edges[4]0.95the tagger's words
concept_edgesmodel-based filtration and DFT —causes→ predictions
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: The combined filtration/DFT flywheel is stated to improve predictions. Audit 2026-09-28: DOUBTFUL - read against its span and kept, but filtration and DFT generate candidates; causes 'predictions' is loose.
concept_edges[5]0.95the tagger's words
concept_edgesincreasing the pretraining dataset —causes→ power-law improvements in accuracy
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: Increasing pretraining scale produces consistent power-law accuracy improvements.
concept_edges[7]0.95the tagger's words
concept_edgesGNoME-pretrained —contrasted-with→ randomly initialized potential
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
the relation itself is separately evidenced · strength of the relation: 1 · note: The figure compares pretrained and randomly initialized potentials under distribution shift.
concept_edges[8]0.95the tagger's words
concept_edgespotential pretrained on GNoME data —causes→ transferability of MLIPs
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direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: The pretrained potential shows systematic, strong transferability improvements over training from scratch.
concept_edges[9]0.95the tagger's words
concept_edgesgraph neural networks (GNNs) —causes→ stable crystals
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 1 · note: Large, diverse first-principles training increases stable-crystal discovery by more than an order of magnitude. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'GNNs'; endpoint abbreviated.
concept_edges[10]0.95the tagger's words
content_tagsGNoME 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]0.95the tagger's words
content_tagsthis work removes at least 5,000 entries that two prior catalogues had called stable
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content_tags.findings[1]0.95the tagger's words
content_tagsthe networks generalise to structures with 5+ unique elements although such structures were omitted from training
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content_tags.findings[2]0.95the tagger's words
content_tagsthe discovered materials are distributed like known stable phases rather than merely filling gaps in the hull
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.findings[3]0.95the tagger's words
content_tagsconvex hull
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: convex hull
content_tags.definitions[0]0.95the tagger's words
content_tagshit rate
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: hit rate
content_tags.definitions[1]0.95the tagger's words
content_tagsexperimental match
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.definitions[2]0.95the tagger's words
content_tags2.2 million structures stable with respect to the Materials Project; 381,000 on the updated hull — None
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[0]0.95the tagger's words
content_tagsat least 5,000 previously 'stable' materials displaced from the Materials Project and OQMD — None
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content_tags.quantities[1]0.95the tagger's words
content_tagsenergies predicted to 11 meV per atom; hit rate above 80% with structure, 33% per 100 trials with composition, against 1% before — None
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[2]0.95the tagger's words
content_tags736 of the ICSD's experimental structures match GNoME discoveries; 184 are novel since the project began — None
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content_tags.quantities[3]0.95the tagger's words
content_tags4,235 composition matches narrowed to 4,180 parsed and then to 736 — None
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[4]0.95the tagger's words
content_tags84% of binaries and ternaries and 86.8% of quaternaries survive r2SCAN — None
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[5]0.95the tagger's words
content_tags45,500 novel prototypes, a 5.6 times increase on the Materials Project's 8,000 — None
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[6]0.95the tagger's words
content_tagslayered materials rise from about 1,000 to about 52,000 — None
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content_tags.quantities[7]0.95the tagger's words
content_tagsthe ICSD holds 20,000 computationally stable structures out of 200,000 entries — None
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content_tags.quantities[8]0.95the tagger's words
content_tagspower-law improvement of test loss with data
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.statistics[0]0.95the tagger's words
content_tagsprecision of stable predictions, reported as a hit rate
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.statistics[1]0.95the tagger's words
content_tagsdecomposition energy
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.variables[0]0.95the tagger's words
content_tagshit rate
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: hit rate
content_tags.variables[1]0.95the tagger's words

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© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824