Paper Citation Record · LEDGER

A Performance and Cost Assessment of Machine Learning Interatomic Potentials

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1906.08888.

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

pith.paper-citation-record.v1
1906.08888 v4

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

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:26:33.377456Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T18:34:48.429024Z

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 baf126af-422f-495e-afc6-55a000821622 · inbound

Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon cites this paper.

Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-24T18:34:48.433663Z

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.

Observation bffd43db-66d2-49e1-8228-ae559899834c · inbound

Machine-learning interatomic potential for radiation damage and defects in tungsten cites this paper.

Machine-learning interatomic potential for radiation damage and defects in tungsten A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T12:26:33.377456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0404d66e-e867-42ce-914c-268f152a661d · inbound

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations cites this paper.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T05:53:18.590161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ae72002-ce4c-4190-9104-311c0cfdd8ca · inbound

Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations cites this paper.

Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T18:58:14.667590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.