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Paper Citation Record · LEDGER

MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.05934.

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

pith.paper-citation-record.v1
2309.05934 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:59:32.991018Z

measured 0 of 1 external citation measurements

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 88c213c1-111a-4cf6-ad64-32096997864b · inbound

Foundational Large Language Models for Materials Research cites this paper.

Foundational Large Language Models for Materials Research MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T16:59:32.991018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:59:32.991018Z digest=sha256:53322f53f37006de6287c10d0589b6306d9ce9f8abd90a83ad42545e3a3d7ef1

Observation bc612fcf-ade0-4fb5-9b47-5cd9fcf147c6 · inbound

Benchmarking large language models for materials synthesis: the case of atomic layer deposition cites this paper.

Benchmarking large language models for materials synthesis: the case of atomic layer deposition MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T16:40:24.304635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:40:24.304635Z digest=sha256:2d427269ab08d7c0528a67b1c6145610021ebde8f432329501be7b04d7402d0c

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.

source=arxiv_source observed=2026-08-09T04:20:16.650132Z digest=sha256:8c1e6af1ff159ea4d88112d930bde5c5546778f2d354993cf9c75c720067add3

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.

source=arxiv_source observed=2026-08-09T04:14:41.948183Z digest=sha256:1654d9de08a45ad6f9f35749150b0e364572d2905c5f628747b747a2c2389e45

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-16T23:16:47.814591Z digest=sha256:7d041ff498ca338b04395bc698c9484186a0ae98eeb5c5ada3404b0873a5eb2d