ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Justin S. Smith, Olexandr Isayev, Adrián E. Roitberg · Chemical Science · 2017 · materials-science · 10.1039/c6sc05720a · record smith2017-ani-1-an-extensible-neural-network
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Other fields define these differently the same word, defined another way in another discipline
RMSE 5 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 12 concept tags on this paper, all 12 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.
transferability ANI-1 baseline 1 NMS ReaxFF SFs DTNN coulomb matrices overtraining network architecture cost function semi-empirical methods untrained parameters reached only by larger inputs atom-type indistinguishability blocking learning trajectory bias in molecular-dynamics sampling cross-molecule overlap covering a sampler's gaps RMSE root mean squared error system-or-tool-building
Method, as described: train atom-type-specific networks on atomic environment vectors, sample conformations by randomly perturbing along normal modes, and test only on molecules larger than any in the training set

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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

The paper

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Every tag on this page

claim_relations 3 · claim_type 1 · concept_edges 2 · concepts 12 · content_tags 14 · contribution 1 · controls 5 · limitations 5 · mechanisms 4 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 4 · transfers 2

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; 2 of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studytransferable atomistic potentials for organic molecules - whether a network trained on quantum-mechanical energies for small molecules can predict energies for larger ones it never saw
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object_of_studythe paper's words
research_questionwhether the accuracy-for-speed trade that empirical potentials have always made can be avoided, rather than merely moved
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research_questionthe paper's words
contributionthe ANI method - atomic environment vectors from modified Behler-Parrinello symmetry functions, plus Normal Mode Sampling - and ANI-1, a potential trained on 17.2 million DFT conformations of molecules up to 8 heavy atoms
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contributionthe paper's words
claim_typemethod
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claim_typethe paper's words
phenomenontransferability - a fitted potential's ability to hold outside the domain it was fitted in, which this field has no systematic way to estimate
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phenomenonthe paper's words
methodsystem-or-tool-building
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methodthe paper's words
mechanismsa sorted coulomb matrix sized for the largest training molecule leaves elements that are constant zero throughout training; a larger test molecule makes those elements non-zero, feeding values into network parameters that were never updated, so the output is arbitrary rather than merely inaccurate
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mechanisms[0]0.95the tagger's words
mechanismssymmetry functions that sum over all neighbours identically leave the network unable to tell a carbon from a hydrogen at a given distance, so the same vector element means different things in different molecules and training cannot resolve it
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mechanisms[1]0.95the tagger's words
mechanismsMD follows one well-defined path across the potential surface, so a data set built from it is a sample of that path rather than of the surface; overcoming the bias requires a trajectory long enough to lose its own starting point
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mechanisms[2]0.95the tagger's words
mechanismsnormal mode sampling can miss interactions in any single molecule, but a data set of many molecules contains the same interaction in other contexts - so accuracy depends on the number of distinct molecules and not only on points per molecule
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mechanisms[3]0.95the tagger's words
limitationsthe headline 0.6 kcal/mol holds only within 30 kcal/mol of each molecule's energy minimum; across the whole sampled range the RMSE is 1.8
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limitations[0]0.95the tagger's words
limitationsthe potential covers only H, C, N and O, and only near-equilibrium conformations, because full sampling of each structure's potential surface is near intractable
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limitations[1]0.95the tagger's words
limitationsNormal Mode Sampling is stated by the authors to be simply an approximation for generating structures, not a sampling of the true potential surface
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limitations[2]0.95the tagger's words
limitationsits accuracy is a property of the training data rather than of the method - the authors say so, and make the improvement path more data rather than a better model
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limitations[3]0.95the tagger's words
limitationsthe error had not plateaued at the largest data set built, so the reported accuracy is a point on a curve still moving rather than a converged property
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limitations[4]0.85the tagger's words
transfersdeep learning architectures and their training machinery - mini-batches, ADAM, max-norm regularisation, early stopping - carried from image and speech recognition into quantum chemistry
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transfers[0]0.95the tagger's words
transfersnormal mode analysis, carried from vibrational spectroscopy into a sampler for generating training conformations
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transfers[1]0.95the tagger's words
controlstesting only on molecules LARGER than any in the training set - every case study uses more than eight heavy atoms, where training stopped at eight
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eliminates: an accuracy that holds only inside the size range the network was trained on · outcome: held
controls[0]0.95the tagger's words
controlstwo baselines carried through the same training - a coulomb-matrix MLP, and an ANI network with atom types NOT differentiated - so the contribution of each design choice is separable
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eliminates: the whole architecture being credited with a gain that the representation or one design choice supplies · outcome: held
controls[1]0.95the tagger's words
controlscomparison against three incumbent semi-empirical methods, DFTB, PM6 and AM1, on every case study rather than against DFT alone
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eliminates: a neural potential compared only against the reference it was trained to imitate, and never against the methods it is meant to replace · outcome: held
controls[2]0.95the tagger's words
controlschecking that the potential surface is SMOOTH and not merely low-error, because a potential with the right energies and the wrong curvature is useless for dynamics
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eliminates: a potential with the right energies and the wrong curvature passing as usable for molecular dynamics · outcome: held
controls[3]0.95the tagger's words
controlstraining-set size varied to check whether accuracy had converged, rather than reporting the largest run alone
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eliminates: an accuracy figure reported from the largest training run without showing whether it had converged · outcome: partial
controls[4]0.95the tagger's words
conceptstransferability
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concepts[0]0.95the paper's words
conceptsANI-1
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also in: ANI-1
concepts[1]0.95the paper's words
conceptsbaseline 1
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also in: baseline 1
concepts[2]0.95the paper's words
conceptsNMS
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: NMS
concepts[3]0.95the paper's words
conceptsReaxFF
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: ReaxFF
concepts[4]0.95the paper's words
conceptsSFs
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: SFs
concepts[5]0.95the paper's words
conceptsDTNN
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also in: DTNN
concepts[6]0.95the paper's words
conceptscoulomb matrices
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concepts[7]0.95the paper's words
conceptsovertraining
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also in: overtraining
concepts[8]0.95the paper's words
conceptsnetwork architecture
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concepts[9]0.95the paper's words
conceptscost function
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also in: cost function
concepts[10]0.95the paper's words
conceptssemi-empirical methods
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concepts[11]0.95the paper's words
sensesRMSE of ANI-1 against DFT - OVER THE WHOLE SAMPLING: 1.8 kcal/mol across 8,245 conformations of 134 ten-heavy-atom molecules
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senses[0]0.95the tagger's words
sensesRMSE of ANI-1 against DFT - WITHIN 30 kcal/mol OF THE MINIMUM: 0.6 kcal/mol. The number the conclusion quotes, and the window is stated with it
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senses[1]0.95the tagger's words
sensesthe better model - ON THE ANI-1 TEST SET: the two baselines perform better than ANI-1
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senses[2]0.95the tagger's words
sensesthe better model - ON THE GDB-10 EXTENSIBILITY SET: ANI-1 at 1.9 kcal/mol against baseline 1 at 493.7. Same models, same training, opposite ranking, and the disambiguator is whether the test molecules are larger than the training ones
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senses[3]0.95the tagger's words
claim_relationsextends → Behler and Parrinello's symmetry-function representation, by adding angular shifts and atomic-number differentiation to make it transferable across chemical environments
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claim_relations[0]0.95the tagger's words
claim_relationscritiques → empirical and semi-empirical potentials, which trade accuracy for speed and offer no systematic way to estimate their own transferability
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claim_relations[1]0.95the tagger's words
claim_relationsqualifies → neural network potentials demonstrated on single systems or short molecular-dynamics trajectories, which were never shown to predict energies for larger molecules
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claim_relations[2]0.95the tagger's words
concept_edgesANI-1 —contrasted-with→ baseline 1
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differs in: total energy RMSE on the GDB-10 extensibility set - 1.9 against 493.7 kcal/mol · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): total energy RMSE on the GDB-10 extensibility set - 1.9 against 493.7 kcal/mol · the relation stated: ANI-1 extends to larger molecules and the coulomb-matrix baseline does not
also in: ANI-1 · baseline 1
concept_edges[0]0.95the tagger's words
concept_edgesnetwork architecture —causes→ overtraining
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direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.8 · direction on the measure (stated by the tagger, no quotation): increases · the relation stated: too large a network generalises badly across structures because it overtrains Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says too large a network can lead to overtraining; the sign holds for size, not architecture.
concept_edges[1]0.95the tagger's words
content_tagsANI-1 is chemically accurate against DFT on molecules up to 54 atoms, much larger than those in its training set.
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content_tags.findings[0]0.95the tagger's words
content_tagsthe RMSE is 1.8 kcal/mol over the whole sampling and 0.6 within 30 kcal/mol of the minimum - the same model under two windows
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content_tags.findings[1]0.95the tagger's words
content_tagsThe baselines do better on the ANI-1 test set but fail to extend to the larger GDB-10 molecules.
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content_tags.findings[2]0.95the tagger's words
content_tagsBaseline 1 fails on larger molecules because coulomb-matrix elements that stayed zero in training become non-zero and feed untrained parameters.
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content_tags.findings[3]0.95the tagger's words
content_tagsthere are no systematic ways for improving or estimating the transferability of empirical potentials
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content_tags.findings[4]0.95the tagger's words
content_tagsExtensibility increases with data set size and had not plateaued at the current size.
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content_tags.findings[5]0.95the tagger's words
content_tagswhether a potential holds outside the systems it was parameterised on; ReaxFF is given as the counter-case, being reparameterised system to system and so lacking an out-of-the-box level of it
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content_tags.definitions[0]0.95the tagger's words
content_tagsNormal Mode Sampling - perturbing an energy-minimised structure along its normal modes to a chosen maximum energy, explicitly an approximation for generating structures
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also in: NMS
content_tags.definitions[1]0.95the tagger's words
content_tagsANI-1's RMSE against DFT over the entire random sampling
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content_tags.quantities[0]0.95the tagger's words
content_tagsthe energy window within which the RMSE falls to 0.6 - the restriction the headline number carries
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content_tags.quantities[1]0.95the tagger's words
content_tagsbaseline 1's total-energy RMSE on the extensibility set, against ANI-1's 1.9
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content_tags.quantities[2]0.95the tagger's words
content_tagsRMSE
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also in: RMSE
content_tags.statistics[0]0.95the tagger's words
content_tagsroot mean squared error
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content_tags.statistics[1]0.95the tagger's words
content_tagsnetwork architecture
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content_tags.variables[0]0.95the tagger's words

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