Paper Citation Record · LEDGER

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.15582.

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pith.paper-citation-record.v1
2502.15582 v3

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measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:06:04.885642Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:08:59.317437Z

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Outbound references

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Pith citing papers

Observation ffaa67f0-a5d5-4896-ab72-ff0a4593c70d · inbound

Fast and Fourier Features for Transfer Learning of Interatomic Potentials cites this paper.

Fast and Fourier Features for Transfer Learning of Interatomic Potentials Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:06:04.885642Z

Source-reported events for the cited work

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Observation e89a1a02-88ef-495f-bbb9-a0d4252d2dba · inbound

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics cites this paper.

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 2379

Resolution: unresolved; no resolver link, observed 2026-08-15T22:42:48.141483Z

Source-reported events for the cited work

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Observation 7793a48a-2e9f-4d72-b1cb-37440fba5cb3 · inbound

Distillation of atomistic foundation models across architectures and chemical domains cites this paper.

Distillation of atomistic foundation models across architectures and chemical domains Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:22.235035Z

Source-reported events for the cited work

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Observation 7419fe5f-78e9-4ff5-b4ff-c55fc26face8 · inbound

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials cites this paper.

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.513861Z

Source-reported events for the cited work

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Observation c2ac60e8-62ac-4328-a532-ae0371a0f4f3 · inbound

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy cites this paper.

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:25:45.257726Z

Source-reported events for the cited work

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Observation d71aa4b7-e999-4bee-9aa2-8ced49f11250 · inbound

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials cites this paper.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.049689Z

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.

Observation 06cef116-76ee-412e-a0c6-04981f674461 · inbound

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry cites this paper.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:07:22.554910Z

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.

Observation 3d6126be-94d9-46c7-bfc2-d48fbf717e2f · inbound

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

Fine-tuning MLIP foundation models: strategies for accuracy and transferability Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.662482Z

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.

Observation db827820-49c1-4541-ba2c-2804bce32c65 · inbound

Revisiting quantum effects on dislocation glide in bcc metals from DFT calculations and machine-learning potentials cites this paper.

Revisiting quantum effects on dislocation glide in bcc metals from DFT calculations and machine-learning potentials Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 132

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T22:08:59.319090Z

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.

Observation c78ec005-71a5-431e-a992-50b7774c72c2 · inbound

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles cites this paper.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T02:31:03.871783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.