Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.05934.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T04:20:16.650132Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T23:18:39.765911Z
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 4385bba0-af32-4c70-bf64-7f8f9a3fa947 · inbound
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models cites this paper.
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:20:16.650132Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 669c6d30-f3f5-4846-8199-85509f7c4f86 · inbound
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:14:41.948183Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5637398e-1c4a-4eec-bc51-d2a71658ba5e · inbound
AI-Driven Expansion and Application of the Alexandria Database cites this paper.
AI-Driven Expansion and Application of the Alexandria Database MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Reference 68
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T23:18:39.767396Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.