Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Universal Machine Learning Interatomic Potentials are Ready for Phonons
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2412.16551.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T04:31:32.535360Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T18:16:16.850658Z
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 ff2e2d13-9c93-4248-9a0a-5c77144cb7da · inbound
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys cites this paper.
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Universal Machine Learning Interatomic Potentials are Ready for Phonons
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:31:32.535360Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7e93e13-20d8-436b-967a-b4bef58ee052 · inbound
Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data cites this paper.
Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Universal Machine Learning Interatomic Potentials are Ready for Phonons
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:37:08.984865Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 957d3175-61c6-49d4-984c-153c2b85285a · inbound
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations cites this paper.
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Universal Machine Learning Interatomic Potentials are Ready for Phonons
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T10:55:21.852727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.