Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.05211.
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 2026-08-04T11:57:52.419384Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 8efce05c-4038-4a1e-a6e0-bde1986f5b8b · inbound
A foundation model for atomistic materials chemistry cites this paper.
A foundation model for atomistic materials chemistry Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
Reference 274
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T10:16:16.437764Z
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.
Observation 404be5f6-9a6b-4b6c-a864-ed06d3853b3f · inbound
Coarse-grained graph architectures for all-atom force predictions cites this paper.
Coarse-grained graph architectures for all-atom force predictions Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T17:56:54.172564Z
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.
Observation 15e02999-54d6-4979-b92a-d41c062c95fb · inbound
Active learning and explicit electrostatics enable accurate modeling of electrolytes cites this paper.
Active learning and explicit electrostatics enable accurate modeling of electrolytes Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T11:57:52.419384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.