Paper Citation Record · LEDGER

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

As of 18 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2505.06462.

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

pith.paper-citation-record.v1
2505.06462 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:17.302346Z

measured 18 of 18 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:14:01.518977Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:14:05.677455Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy8
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c64971f-69a8-4109-972e-ea6b4d6e4ad8 · outbound

This paper cites Schr\"odinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Schr\"odinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T22:46:17.541307Z

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.

Observation 32d42c9c-2cdf-444b-8816-3d3eee928bab · outbound

This paper cites P.; Chodera, J.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties P.; Chodera, J

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.595284Z

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.

Observation 58666970-fdd9-449f-85e0-14f4c52e589b · outbound

This paper cites S.; Iyer, S.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties S.; Iyer, S

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.571972Z

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.

Observation beee9366-7956-408d-8919-c866752ad04d · outbound

This paper cites The radial distribution functions of water and ice from 220 to 673 K and at pressures up to 400 MPa.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties The radial distribution functions of water and ice from 220 to 673 K and at pressures up to 400 MPa

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.583483Z

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.

Observation d2a5ad1a-344a-49f4-b003-b8aa251cd86c · outbound

This paper cites Generalized Neural-Network Representation of High- Dimensional Potential-Energy Surfaces.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Generalized Neural-Network Representation of High- Dimensional Potential-Energy Surfaces

Reference 134

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.635349Z

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.

Observation 74f59bb8-ca27-4935-bd65-aaae5a71fa0a · outbound

This paper cites M.; Mori, T.; Mizukami, W.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties M.; Mori, T.; Mizukami, W

Reference 154

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.272484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e689ba-4b01-4072-a4c3-b7be76267328 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Orb: A Fast, Scalable Neural Network Potential

Reference 185

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.258446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ee4e1f5-c0de-4c4a-a4db-357bae3a0520 · outbound

This paper cites PET-MAD, a lightweight universal interatomic potential for advanced materials modeling.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 265

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.302346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5337597c-d003-49ca-8334-f7af458612a3 · outbound

This paper cites Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 293

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.262278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d233127-ace4-4e58-8250-e3eaff94c2ac · outbound

This paper cites Machine learning potentials for redox chemistry in solution.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Machine learning potentials for redox chemistry in solution

Reference 398

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.254661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ac9bca7-72f0-4d4c-9eda-07b5a5c540e7 · outbound

This paper cites System- atic assessment of various universal machine-learning interatomic potentials.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties System- atic assessment of various universal machine-learning interatomic potentials

Reference 1093

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.552010Z

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.

Observation d1b1dfb0-8227-497d-b44d-cda4745cd679 · outbound

This paper cites S.; Fung, V.; Huck, P.; O’Donnell, C.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties S.; Fung, V.; Huck, P.; O’Donnell, C

Reference 1597

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.561879Z

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.

Observation 7e81d7c2-d141-4e59-a165-49e8e876842d · outbound

This paper cites A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.606016Z

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.

Observation ea554c83-44b9-4d18-bbc6-880b00d48e55 · outbound

This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:17.282314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f357d6c-7bd6-47d2-9d3f-32a28e9610af · outbound

This paper cites E.; Lubbers, N.; Matin, S.; Smith, J.; Messerly, R.; Tretiak, S.; Barros, K.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties E.; Lubbers, N.; Matin, S.; Smith, J.; Messerly, R.; Tretiak, S.; Barros, K

Reference 2991

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:17.615833Z

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.

Observation ce06b1b7-ccdb-4c09-aebe-26b1e6d3bac5 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 4870

Resolution: malformed identifier; no resolver link, observed 2026-08-15T22:46:17.289063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29fc4945-6211-463c-8f6a-901dcda4d46b · outbound

This paper cites an unresolved cited work.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Unresolved cited work

Reference 6424

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:46:17.625539Z

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.

Pith citing papers

Observation 9b885076-f71b-45d1-b851-4a2481866e40 · inbound

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials cites this paper.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:14:05.767093Z

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.