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Paper Citation Record · LEDGER

Electrostatic interactions in atomistic and machine-learned potentials for polar materials

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.01642.

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

pith.paper-citation-record.v1
2412.01642 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:48:58.073361Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:14:06.835630Z

Reference resolution

0 of 0 outbound references displayed

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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 b12e1577-a4f9-405e-afb9-5a0212cae1ee · inbound

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

Learning charges and long-range interactions from energies and forces Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T11:30:48.017127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:30:48.017127Z digest=sha256:7a8c84aeffb376ad2a24921f0298cbd4552709e49248a86aa2d71a3b69ec8cd4

Observation 923c467f-4f3c-477e-81fc-8ad98e6bbfc1 · inbound

Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials cites this paper.

Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-16T10:48:58.073361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:48:58.073361Z digest=sha256:1971951055ed91fb4c1c8fc27bea9ffff6883969bcee013fee9fd4f0c8e7727d

Observation 2d1a4173-789b-466f-b984-40f8538e8e27 · inbound

Efficient local atomic cluster expansion for BaTiO$_3$ close to equilibrium cites this paper.

Efficient local atomic cluster expansion for BaTiO$_3$ close to equilibrium Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:55.134157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:40:55.134157Z digest=sha256:751826aa9a52f73668c8c1f13ee534eaeed2ca36365ca435bf15917f62587e05

Observation c5e68233-2652-4fda-9aed-e1b2441c8fe1 · inbound

Machine Learning the Energetics of Electrified Solid/Liquid Interfaces cites this paper.

Machine Learning the Energetics of Electrified Solid/Liquid Interfaces Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T14:12:56.537782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:12:56.537782Z digest=sha256:1c634f0a329b8bdf3b6435ab519f44ec6a8115f1b5392fdf6205eaaaddad0a91

Observation ae86c640-2c39-4de9-bd6a-e9c81cc06962 · 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 Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-06T16:14:06.901792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T16:13:52.200940Z digest=sha256:e7dfba1b85a47c9e9c72b3682a6fcb02eb854a26afe904ebd3eb5864ac8888b4