Paper Citation Record · LEDGER

MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2312.15211.

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

pith.paper-citation-record.v1
2312.15211 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:14:08.528057Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:38:19.666989Z

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 de55802a-d7b8-4e99-aae5-f362d34a9061 · inbound

A foundation model for atomistic materials chemistry cites this paper.

A foundation model for atomistic materials chemistry MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 108

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T10:16:16.415274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 39b87aa5-ed40-4c6c-9193-b167b8d6efeb · inbound

AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials cites this paper.

AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:11:40.367332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2ac640fe-02af-4493-96fc-f7a83ee1b1ca · inbound

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids cites this paper.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T19:14:08.528057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0d77846-9748-42b5-bf6f-76ead7091601 · inbound

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures cites this paper.

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:09.723626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a36dae5-a450-4bba-b0c2-fd5c89410ff4 · inbound

Hierarchical generative modeling for the design of multi-component systems cites this paper.

Hierarchical generative modeling for the design of multi-component systems MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:15:28.929695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cd6defde-99d6-4c24-aef8-bfb0f8a3338d · inbound

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:51:18.067343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c751a93c-5655-4447-a133-7360f6ed493f · inbound

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:40.295678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bd0c1098-adc9-4574-a00d-58402b0b749d · inbound

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 110

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.708841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2371f1e3-add4-47c1-a10b-95b318a73031 · inbound

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 110

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:47:40.310735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 924e3b18-39e8-4a66-9e66-4afddc12c762 · inbound

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution cites this paper.

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:26:24.155682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5db6eb35-dc3f-4ed1-8511-f52e03808e7a · inbound

Fine-tuning MLIP foundation models: strategies for accuracy and transferability cites this paper.

Fine-tuning MLIP foundation models: strategies for accuracy and transferability MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:38:19.668367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6f3c6d42-80ff-4e1f-a046-22d9ab1ded6b · inbound

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations cites this paper.

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T12:39:52.699352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.