Paper Citation Record · LEDGER

Transferability of datasets between Machine-Learning Interaction Potentials

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2409.05590.

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

pith.paper-citation-record.v1
2409.05590 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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:40:14.611269Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:16:16.439862Z

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 1bb7aa39-b975-4cc5-bc6d-680b5f460876 · inbound

A foundation model for atomistic materials chemistry cites this paper.

A foundation model for atomistic materials chemistry Transferability of datasets between Machine-Learning Interaction Potentials

Reference 273

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:16:16.442757Z

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.

Observation 7a2820ca-5a3a-41e6-ad07-f451f6f35bcd · inbound

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) cites this paper.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Transferability of datasets between Machine-Learning Interaction Potentials

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:14.611269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6322ee57-eb6b-40c0-a993-2395a46f2eb7 · inbound

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Transferability of datasets between Machine-Learning Interaction Potentials

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:12.739411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d65ff0bf-639a-40b2-8be5-9df1df128efd · inbound

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries cites this paper.

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries Transferability of datasets between Machine-Learning Interaction Potentials

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:21:09.982358Z

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.