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Paper Citation Record · LEDGER

Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.14655.

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

pith.paper-citation-record.v1
2105.14655 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T06:01:04.355118Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T13:08:50.367997Z

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 63bae921-648a-44c3-95cc-0be3247909f8 · inbound

OpenQDC: Open Quantum Data Commons cites this paper.

OpenQDC: Open Quantum Data Commons Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T06:01:04.355118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T06:01:04.355118Z digest=sha256:a78810e882a9623b214323739cc00bb2ca98bb013d499ec74b498c200099f192

Observation 11abc639-c99c-4317-97a6-1dc3e1cc4969 · inbound

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials cites this paper.

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry

Reference 176

Resolution
verified exact
local_arxiv, observed 2026-08-08T13:08:50.372871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-08T13:08:49.733233Z digest=sha256:2f4e55f32194939984467b5fa40348e716d021c33fa85e622bade5e0c38ee21e