Paper Citation Record · LEDGER

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2402.13984.

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

pith.paper-citation-record.v1
2402.13984 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:07:35.694447Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T18:58:19.950415Z

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 653622af-ba02-49c8-b31f-22207863b404 · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:42:26.305868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 95b64ce7-e61c-46f1-9039-bb3afc1a7b2b · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:58:19.953536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bf5fa89a-c73a-4f92-a154-9cfa6d855754 · inbound

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

Implicit Delta Learning of High Fidelity Neural Network Potentials Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:07:35.694447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f628220d-d757-4de6-9531-fbd56a633fac · inbound

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians cites this paper.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:18:33.929625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6251316e-86cb-4c23-bdce-0d80139856ad · 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 Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T11:08:29.822868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c922e226-1a26-4304-8228-6b6109e34d9b · inbound

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.302285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aba346ef-71c7-4285-9a85-71a01e36b868 · inbound

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations cites this paper.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:21.590406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.