Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.04217.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:40.849539Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T10:06:52.109952Z
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 1cf15fb8-75af-4f66-a433-177363ec11eb · inbound
Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture cites this paper.
Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:57:40.849539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ec566479-a5db-4523-b19a-165e2f5c0589 · inbound
SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials cites this paper.
SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T10:06:52.111254Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.