Paper Citation Record · LEDGER

AI-driven materials design: a mini-review

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.02905.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02905 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:05:10.009650Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:29:44.420670Z

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 8ad3056b-e03a-4922-bcaa-cf18c7054e5a · inbound

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design cites this paper.

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design AI-driven materials design: a mini-review

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:30:49.223518Z

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.

Observation e96241a5-ff44-4ced-8c8f-0f48e6a1683d · inbound

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling cites this paper.

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling AI-driven materials design: a mini-review

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:05:10.009650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24ed9750-9d60-4a13-9a63-10c8fdbf4c29 · inbound

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry AI-driven materials design: a mini-review

Reference 272

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:10.241642Z

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.

Observation 87739492-5d9e-4198-b371-066b292ff9e9 · inbound

General-purpose LLMs as Constrained Crystal Composition Generators cites this paper.

General-purpose LLMs as Constrained Crystal Composition Generators AI-driven materials design: a mini-review

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:11.228299Z

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.

Observation 0a5cff21-e937-49c6-810a-043de39fc225 · inbound

Charting the emergent low-dimensional manifold of quantum materials cites this paper.

Charting the emergent low-dimensional manifold of quantum materials AI-driven materials design: a mini-review

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:18.527472Z

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.

Observation d217ca79-c159-4a08-9762-a7dd1301d64d · inbound

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials cites this paper.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials AI-driven materials design: a mini-review

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.422072Z

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.