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Paper Citation Record · LEDGER

Learning Interatomic Potentials at Multiple Scales

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.13756.

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

pith.paper-citation-record.v1
2310.13756 v1

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:26:55.057639Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:37:19.045160Z

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 8cd58ee7-f080-4fbb-af84-34dd1c5da08d · inbound

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials cites this paper.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Learning Interatomic Potentials at Multiple Scales

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-02T22:26:55.057639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:26:55.057639Z digest=sha256:bf2038f4eaf651da150de797396f22cdabf81b8932f837498543460f38e40596

Observation 27d70ba3-9d16-4890-9a6d-98c108fd061a · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Learning Interatomic Potentials at Multiple Scales

Reference 239

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:37:19.046708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T21:27:50.941166Z digest=sha256:79a469d99873cf9694f9eb7a2206aa63aa73c90aa0afa8a2675d41583ac88ecc