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Paper Citation Record · LEDGER

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2603.05238.

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

pith.paper-citation-record.v1
2603.05238 v2

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T16:17:26.963678Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:06:05.134103Z

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 14479388-5753-44b0-827e-743aef50fd7c · inbound

Accelerating point defect simulations using data-driven and machine learning approaches cites this paper.

Accelerating point defect simulations using data-driven and machine learning approaches Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-06-09T02:06:15.299570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-09T23:29:55.933661Z digest=sha256:57b0b13d9aca2552c3bfce2a1757dcd0d2c1d4eef5864f0e2496ec1c9fd3cdc4

Observation 59683666-0ee1-485d-a440-8b70fbd0f349 · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

Reference 115

Resolution
unresolved
no resolver link, observed 2026-07-11T16:17:26.963678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T16:17:26.963678Z digest=sha256:e4b487f0b3c8f7848ff96b9fe6977e45f96989a0eea5aeb92e5c77ad1bb7cda5