Paper Citation Record · LEDGER

GemNet: Universal Directional Graph Neural Networks for Molecules

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.08903.

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

pith.paper-citation-record.v1
2106.08903 v10

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:26:30.672518Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

127
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3d80db0a-82ce-4bd8-b6cf-dd8dca30d90c · inbound

Tokenizing Electron Cloud in Protein-Ligand Interaction Learning cites this paper.

Tokenizing Electron Cloud in Protein-Ligand Interaction Learning GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:30.672518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78c64267-df00-475b-b57c-95156a447bed · inbound

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems cites this paper.

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:21.804757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0298957-2b68-4b3e-850c-778584b04108 · inbound

Spatial statistics for screening molecular structures cites this paper.

Spatial statistics for screening molecular structures GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:23:21.571303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af43cdcb-7156-4e57-abee-0e6810074fb9 · inbound

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation cites this paper.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.254310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 82868ba4-498a-4099-a0e9-3f820ed23298 · inbound

High-order tensor neural network for iteration-free structure relaxation cites this paper.

High-order tensor neural network for iteration-free structure relaxation GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-30T04:04:17.688789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 447d795a-f109-416c-83b3-375b460ab5b5 · 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 GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.414689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.