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Paper Citation Record · LEDGER

Accurate global machine learning force fields for molecules with hundreds of atoms

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.14865.

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

pith.paper-citation-record.v1
2209.14865 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:32:43.095209Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:21:54.877023Z

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 ea5d2a63-11af-4af5-a123-4c45238ab7de · inbound

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning cites this paper.

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning Accurate global machine learning force fields for molecules with hundreds of atoms

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T18:32:43.095209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:32:43.095209Z digest=sha256:175fb1434a85d58ebc1964a90e584616c415262cc9c23e59b58aae46e0b6ee96

Observation a1b3f30b-6e8c-492a-875e-2858bcc03010 · inbound

StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer cites this paper.

StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer Accurate global machine learning force fields for molecules with hundreds of atoms

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-15T18:21:54.883612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T18:21:54.585400Z digest=sha256:f69778cd851017f655fbcabb313c7aa3d5a254cae1859896f61930b66720b6b6