SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions

Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller · NeurIPS / arXiv · 2017 · chemistry · record schutt2017-schnet
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Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. Two quotes preserve PDF line-break artefacts that must not be tidied: 'continuousfilter' and 'quantumchemical' are run-together in the source. discipline is given as machine-learning because the contribution is an architecture; the application domain (quantum chemistry) is carried by the phenomenon facet. The paper's criticism of enn-s2s (Gilmer et al. [21]) - that discontinuities in its potential energy surface rule it out for MD17 - is a limitation of prior work rather than one of the four relation types, so it is not encoded. 2026-08-14, 2026-08-15 and 2026-08-25 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node. Update, 15 September 2026: EXCEPTION, measured with the merge gate's own predicate (Paper.locate): 1 quote(s) on this record are not found as one exact string in the current staged text - content_tags.statistics[1].evidence. The sentence above does not hold for them as written. Where a row records why - the quote is grounded in another copy of the paper, joins two passages with an ellipsis, or differs from the staged text by extraction noise - that note stays on the row.

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Other fields define these differently the same word, defined another way in another discipline
deep learning 11 fields · atoms 2 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 10 concept tags on this paper, 6 are grounded in a quoted sentence.

molecular property prediction neural network potential quantum chemistry deep learning continuous-filter convolution potential energy surface interatomic forces molecular energy rotational invariance QM9 continuous-filter convolutions over continuous atom positions (not a grid), preserving the physical information in molecular geometry molecules atoms energy quantum convolutional forces chemical interatomic continuous-filter continuous-filter convolutional layer total energy system-or-tool-building

Legend

relation to other work	`claim_relations`	how this paper stands to another: agrees, contradicts, extends, qualifies
claim type	`claim_type`	the shape of the claim: existence, mechanism, method, critique, prediction, null
concept edge	`concept_edges`	an asserted relation between two concepts, with the sentence asserting it
concept	`concepts`	an idea the paper works with, quoted where the paper uses it
content tag	`content_tags`	a finding, definition, quantity, statistic or variable named in the paper
control	`controls`	an experiment run to eliminate a rival explanation
limitation	`limitations`	what the paper says its evidence cannot support
mechanism	`mechanisms`	how the reported effect is said to come about
method	`method`	how the paper produced its evidence
phenomenon	`phenomenon`	the effect or regularity under study
sense	`senses`	a distinct use of a term, quoted where the paper fixes it
transfer	`transfers`	a method or concept carried in from another field

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://arxiv.org/abs/1706.08566. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 5 · claim_type 1 · concept_edges 2 · concepts 6 · content_tags 7 · controls 1 · limitations 7 · mechanisms 1 · method 1 · phenomenon 1 · senses 3 · transfers 1

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

facet	tag	field	conf.	whose words
claim_type	method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_type`		the paper's words
phenomenon	modeling quantum interactions in molecules Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`phenomenon`		the paper's words
method	system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: system-or-tool-building	`method`		the paper's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: continuous-filter convolutions over continuous atom positions (not a grid), preserving the physical information in molecular geometry	`mechanisms[0]`		the tagger's words
limitations	chemical compound space is vast and accurate quantum calculations are costly, preventing exhaustive exploration Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any claim of coverage over chemical space: Quotation withheld [exploration]	`limitations[0]`		the tagger's words
limitations	The radial-basis filter parameters (number and range of centres) were not systematically studied. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the reported architecture choices: AN EXTENSIVE EVALUATION OF THE IMPACT OF THESE VARIABLES IS LEFT FOR FUTURE WORK	`limitations[1]`		the tagger's words
limitations	Training on forces roughly doubles the computational cost, because forces require a full forward and backward pass through the energy model. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the force-training results, which are not free: WE EXPECT TO SEE IMPROVED GENERALIZATION, HOWEVER, AT A COMPUTATIONAL COST	`limitations[2]`		the tagger's words
limitations	Performance on the new ISO17 benchmark, which combines chemical and conformational variation, is only promising rather than solved. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the ISO17 benchmark result: WHILE WE ACHIEVE PROMISING RESULTS ON THIS NEW BENCHMARK, MODELING CHEMICAL AND CONFORMATIONAL VARIATIONS REMAINS DIFFICULT AND NEEDS FURTHER IMPROVEMENT	`limitations[3]`		the tagger's words
limitations	A whole class of competing methods is excluded from force training by its REPRESENTATION - discrete binning and one-hot bond types - which makes their energy surface discontinuous. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: a comparison of this model against circular fingerprints, graph convolutions or message-passing networks on forces: those methods cannot be trained with forces at all, because discrete binning or one-hot bond types put discontinuities in their potential energy surface	`limitations[4]`	1.0	the tagger's words
limitations	The main benchmark contains only equilibrium molecules, where the force term the paper exists to predict is zero by definition. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: a QM9 result as evidence about force prediction: QM9 holds only equilibrium molecules, so the forces there are zero by definition and are not predicted at all	`limitations[5]`	1.0	the tagger's words
limitations	A named competitor is ruled out of one benchmark entirely, and its scaling behaviour is given as quadratic in both atoms and examples. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: GDML as a baseline: its kernel matrix grows quadratically in both the number of atoms and the number of examples, and it is stated elsewhere in the paper that it cannot be used for the molecular-dynamics predictions	`limitations[6]`	1.0	the tagger's words
transfers	deep convolutional neural networks applied to model quantum interactions in molecules Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`transfers[0]`		the tagger's words
controls	comparison of force and energy prediction against a mean predictor as a baseline, on held-out MD trajectories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: that SchNet's reported accuracy could be obtained by predicting the dataset mean, and that it comes from memorising conformations rather than generalising · outcome: held	`controls[0]`	0.85	the tagger's words
concepts	continuous-filter convolution Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: continuous-filter convolution	`concepts[4]`		the paper's words
concepts	potential energy surface Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: potential energy surface	`concepts[5]`		the paper's words
concepts	interatomic forces Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: interatomic forces	`concepts[6]`		the paper's words
concepts	molecular energy Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: molecular energy	`concepts[7]`		the paper's words
concepts	rotational invariance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: rotational invariance	`concepts[8]`		the paper's words
concepts	QM9 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: QM9	`concepts[9]`		the paper's words
senses	the invariances - BY CONSTRUCTION, not measured: rotational equivariance of the forces follows from rotational invariance of the energy plus the architecture, so it is a guarantee rather than an empirical result, and must not be compared with a model's measured conservation Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[0]`	1.0	the tagger's words
senses	QM9 - AS A BENCHMARK OF THE SPECIAL CASE: equilibrium molecules only, which is precisely where the forces vanish, so the benchmark omits the regime the architecture was built for Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[1]`	1.0	the tagger's words
senses	the filter - CONTINUOUS RATHER THAN DISCRETE, because atom positions do not lie on a grid and their precise locations carry physical information that discretising destroys Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[2]`	1.0	the tagger's words
claim_relations	extends → grid-based convolutional neural networks Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[0]`		the tagger's words
claim_relations	extends → dynamic filter networks [34] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[1]`		the tagger's words
claim_relations	extends → deep tensor neural networks (DTNN), Schütt et al. [20] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[2]`		the tagger's words
claim_relations	builds-on → gradient-domain machine learning (GDML), Chmiela et al. [19] Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[3]`		the tagger's words
claim_relations	builds-on → ResNet [36] residual connections Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[4]`		the tagger's words
concept_edges	continuous-filter convolution —causes→ potential energy surface 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 · the relation stated: the continuous-filter layers are what YIELD a continuous energy surface Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'continuous-filter convolutional layers', not 'continuous-filter convolution'. also in: continuous-filter convolution · potential energy surface	`concept_edges[0]`		the tagger's words
concept_edges	interatomic forces —operationalised-as→ molecular energy Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says forces are RELATED to energy and obtained by differentiating it; operationalised-as is loose. also in: interatomic forces · molecular energy	`concept_edges[1]`		the tagger's words
content_tags	three — benchmark datasets used (QM9, MD17, ISO17) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.quantities[0]`		the tagger's words
content_tags	SchNet yields a joint model of total energy and interatomic forces that follows fundamental quantum-chemical principles. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.	`content_tags.findings[0]`		the tagger's words
content_tags	SchNet delivers rotationally invariant energy predictions and rotationally equivariant force predictions. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.findings[1]`		the tagger's words
content_tags	total energy Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: total energy	`content_tags.variables[2]`		the tagger's words
content_tags	interatomic forces Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: interatomic forces	`content_tags.variables[3]`		the tagger's words
content_tags	continuous-filter convolutional layer Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: continuous-filter convolutional layer	`content_tags.definitions[0]`		the tagger's words
content_tags	potential energy surface Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: potential energy surface	`content_tags.definitions[1]`		the tagger's words

Interpretive choices

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