Paper Citation Record · LEDGER

Machine learning interatomic potential can infer electrical response

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.05169.

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

pith.paper-citation-record.v1
2504.05169 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:41.787798Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T16:54:16.404723Z

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 01aa5209-d94f-4d37-84a0-fb206f93102b · inbound

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials cites this paper.

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials Machine learning interatomic potential can infer electrical response

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:41.787798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65647b1d-91ca-4072-82bc-fd1ced5a15c2 · 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 Machine learning interatomic potential can infer electrical response

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:55.116000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b1f1ac-b71c-4b8c-ace6-414a78f009ca · inbound

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects cites this paper.

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects Machine learning interatomic potential can infer electrical response

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:41:52.902824Z

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 b7fd840b-d570-4a55-b567-906244f6fab5 · inbound

Simultaneous Learning of Static and Dynamic Charges cites this paper.

Simultaneous Learning of Static and Dynamic Charges Machine learning interatomic potential can infer electrical response

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:54:16.407411Z

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.