Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1809.02723.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:38.452784Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T10:01:28.020938Z
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 b4fa3b55-b880-4af0-802f-c8f2c3a3bd27 · inbound
Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement cites this paper.
Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T23:02:29.573599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 153636bb-9e62-4ac7-be9d-b0afad072dd9 · 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 Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:26:38.452784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.