Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Does Hessian Data Improve the Performance of Machine Learning Potentials?
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.07839.
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 2026-08-16T05:20:43.569390Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:37:18.968377Z
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 bb311c5a-cb6d-4d9a-9bd0-7cc9b9680d39 · inbound
Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation cites this paper.
Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation Does Hessian Data Improve the Performance of Machine Learning Potentials?
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:20:43.569390Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 447ab447-f658-4852-a9e6-45e892766ea4 · inbound
HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials cites this paper.
HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Does Hessian Data Improve the Performance of Machine Learning Potentials?
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:38:46.821704Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8ccb0e9-81f1-4c5f-bda4-ce7951ec80a3 · inbound
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Does Hessian Data Improve the Performance of Machine Learning Potentials?
Reference 205
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:37:18.969825Z
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.