Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.03578.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:40:14.271737Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T13:03:41.061625Z
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 e035f326-d1af-4e49-98cd-db376f283cb9 · 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) Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:40:14.271737Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b5144d0-499b-454d-9265-9d92f2edb92b · 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 Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:23:12.448363Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a249f2f3-e661-43ab-8283-63b319dc7c68 · inbound
Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models cites this paper.
Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T13:03:41.230370Z
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.