Paper Citation Record · LEDGER

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

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 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 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:46.521183Z

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

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External citation measurements

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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 2026-08-15T06:32:42.880941+00:00.

Observation a6f41815-c6ec-4b00-a918-4796357e7c80 · inbound

A potassium ion channel simulated with a universal neural network potential cites this paper.

A potassium ion channel simulated with a universal neural network potential MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T10:50:54.459651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 411ac351-f6d3-48cf-a5c3-a9da88245a43 · inbound

OpenQDC: Open Quantum Data Commons cites this paper.

OpenQDC: Open Quantum Data Commons MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T06:01:04.382797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b9652bd-aa2a-49d0-bc8c-6ac3b014b3a8 · inbound

Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution cites this paper.

Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T06:01:01.108525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9daf7ccd-d2c0-4ad1-ada0-b6a7aa4f5a0e · inbound

Frontier orbitals control dynamical disorder in molecular semiconductors cites this paper.

Frontier orbitals control dynamical disorder in molecular semiconductors MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T20:09:43.652780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22b86c9a-1627-453c-96f1-231af9185f69 · inbound

Implicit Delta Learning of High Fidelity Neural Network Potentials cites this paper.

Implicit Delta Learning of High Fidelity Neural Network Potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:07:35.634521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c94d2c4f-3116-4433-b002-63850dfd151b · inbound

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps cites this paper.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.125669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 113e163d-e4b4-4e37-ac01-25e70af82cab · inbound

SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System cites this paper.

SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T00:44:30.044902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ded06040-1216-46bb-90eb-5ae592a3bcca · inbound

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials cites this paper.

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:05.691985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fdc10ef-62fa-451b-a159-35aebd92f6a1 · 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 MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.521183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6709f60f-14e3-440b-be01-b7b52dab7bc1 · inbound

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data cites this paper.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:09.073074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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.