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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:37:08.984865Z
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 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 2026-08-08T06:32:00.761636+00:00.