Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Capturing long-range interaction with reciprocal space neural network
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.16684.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T21:05:47.549751Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:14:06.636350Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation cf76cc80-59ed-4c87-8fb9-2d7824c4f451 · inbound
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration cites this paper.
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration Capturing long-range interaction with reciprocal space neural network
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T21:05:47.549751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f5f8e05-ebbf-4d44-9a7c-7bcdf8b4d69a · inbound
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction cites this paper.
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Capturing long-range interaction with reciprocal space neural network
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:21:13.615891Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d38cabfc-0183-4a0f-b47c-21a57da03606 · inbound
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials cites this paper.
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Capturing long-range interaction with reciprocal space neural network
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:14:06.702503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.