Paper Citation Record · LEDGER

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1706.08566.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1706.08566 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:05:34.896823Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

470
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6b753c41-0dd3-4783-92e7-30bc2042f99f · inbound

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction cites this paper.

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T18:05:34.896823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23510cf8-0a1e-4ade-9af2-ec3c19f0033f · inbound

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials cites this paper.

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:28:47.616267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aae34718-0394-4554-a4ed-976c271c1c59 · inbound

Machine learning the single-$\Lambda$ hypernuclei with neural-network quantum states cites this paper.

Machine learning the single-$\Lambda$ hypernuclei with neural-network quantum states SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T04:24:13.629002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a781d7a-3921-46f5-9dde-7e534b250270 · inbound

InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames cites this paper.

InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T07:22:16.065327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 713ffb0f-0873-4d4d-b377-b43910c237a6 · inbound

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction cites this paper.

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T19:57:53.709608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ffcec07c-1790-42f9-bb00-50b6b5b50fad · inbound

Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations cites this paper.

Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-06-28T19:22:34.892762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c6e37ca-95a6-4360-b7eb-fc0cac9081b0 · inbound

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials cites this paper.

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-06-28T03:31:30.133919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3cfd5901-270a-48c4-96ca-e07d911808a6 · inbound

Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration cites this paper.

Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-06-27T14:40:57.959101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bbc6e2e3-6c11-4913-a683-4113047fe4b4 · inbound

MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction cites this paper.

MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-06-26T18:09:41.092068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 347ccf39-16cb-4714-8479-7c6330739e3f · inbound

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models cites this paper.

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T11:16:29.597298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94fe382-c10c-48f4-a36f-c54cdc5bde43 · inbound

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning cites this paper.

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T18:33:45.074792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d75feb-0c9d-4d83-a855-9b0eb7de592d · inbound

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density cites this paper.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.402961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5120a11e-3cc9-4646-8a02-7ac4b8a48a69 · inbound

Next Generation of Ultra-Coarse-Graining: Self-Consistent Inference of Critical Internal States cites this paper.

Next Generation of Ultra-Coarse-Graining: Self-Consistent Inference of Critical Internal States SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T14:10:57.904334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.