Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2505.22397.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-22T03:33:02.264346Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T03:34:34.260464Z
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 3737594e-ab08-45f5-87f4-afdb59442ad3 · inbound
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:51:18.101217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7f37babf-3270-430a-83ac-d264191b80ff · inbound
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:47:40.281881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d9212eea-67e4-4493-ad98-77668b27da48 · inbound
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:12:50.684644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0f1ea8e1-0268-43f5-9345-d9a3d57d1987 · inbound
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:47:40.330397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 693f18d3-72f8-49c1-9725-6e0d5fd478d6 · inbound
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation cites this paper.
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T03:34:34.264003Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.