Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.08177.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:03.913572Z
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
3
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 bfd59ce3-2abb-4223-9f44-28d5b9d155c7 · inbound
Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations cites this paper.
Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:30:03.913572Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdff6a94-108e-4c02-9c4a-8e1e7749896e · inbound
NEPMaker: Active learning of neuroevolution machine learning potential for large cells cites this paper.
NEPMaker: Active learning of neuroevolution machine learning potential for large cells Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T12:20:22.371740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.