Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.03837.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:28:52.181916Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:14:06.013708Z
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 78613ff9-7179-4ea6-82f2-1998d27f0a37 · inbound
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials cites this paper.
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:28:52.181916Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1df27e3-e431-40b8-b8a6-14de1c62fbe3 · inbound
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials cites this paper.
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:14:06.125181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.