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Paper Citation Record · LEDGER

Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.17740.

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

pith.paper-citation-record.v1
2407.17740 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:48.159596Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:28:52.463764Z

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 2299fd21-e525-48d9-8388-21c850194897 · inbound

Learning charges and long-range interactions from energies and forces cites this paper.

Learning charges and long-range interactions from energies and forces Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T11:30:48.159596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:48.159596Z digest=sha256:ac71ac1df3360b4395acd770bfb1afafec4e8a1c95562be2ff47f14e0f57e502

Observation aedf915c-e926-47af-97af-d672d7eab6c5 · inbound

Machine learning accelerated finite-field simulations for electrochemical interfaces cites this paper.

Machine learning accelerated finite-field simulations for electrochemical interfaces Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:28:52.608288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T04:28:49.302653Z digest=sha256:cb18ae11c9b292e82cd488a2b461575e49c6c225206b4305a122e36fa5a94304