Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the design space between molecular mechanics and machine learning force fields
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.01931.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:33:33.066203Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:53:03.332109Z
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 28a5adce-1bc0-4e03-83af-1f06ad98226a · inbound
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks cites this paper.
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks On the design space between molecular mechanics and machine learning force fields
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:33:33.066203Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a0c3c7a-d310-4bb2-93fa-7b9a30f421d2 · inbound
NepoIP/MM: Towards Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects cites this paper.
NepoIP/MM: Towards Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects On the design space between molecular mechanics and machine learning force fields
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:08:29.835578Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ac429b7-6d2b-4557-b48f-93ce31855ed6 · inbound
Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions cites this paper.
Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions On the design space between molecular mechanics and machine learning force fields
Reference 69
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T21:53:03.390272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.