Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A deep learning approach to search for superconductors from electronic bands
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.07721.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:41:09.020579Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T00:50:50.897377Z
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 23c45b7a-a92d-428c-8870-403cee02129e · inbound
AI-driven inverse design of materials: Past, present and future cites this paper.
AI-driven inverse design of materials: Past, present and future A deep learning approach to search for superconductors from electronic bands
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T20:41:09.020579Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64c5599f-bf24-493c-b205-23c7b7c57c42 · inbound
Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor cites this paper.
Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor A deep learning approach to search for superconductors from electronic bands
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:18:27.374016Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d38c10e3-f573-4db8-9811-235fe5f93e10 · inbound
HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction cites this paper.
HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction A deep learning approach to search for superconductors from electronic bands
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T00:50:50.900330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.