Paper Citation Record · LEDGER

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2405.07105.

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

pith.paper-citation-record.v1
2405.07105 v1

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 2026-08-19T06:32:44.657259+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:20:43.553175Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T17:24:00.448248Z

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 caa0ab22-0b85-4400-bdf3-2c1479735422 · inbound

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation cites this paper.

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T05:20:43.553175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30742a4d-53ae-47fa-a3f9-e44c323d5a63 · inbound

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

Fast and Fourier Features for Transfer Learning of Interatomic Potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T23:06:04.889279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b4f72b-25a8-4413-940a-fb9010770482 · inbound

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) cites this paper.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:13.950423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eb709c5-6441-4184-a170-042266b86a63 · inbound

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields cites this paper.

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:53.006211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e936c15c-85d4-4ca2-b656-bf06ae022665 · inbound

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing cites this paper.

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 52

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T10:02:31.786236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ec76daf-1644-4843-9a9c-20c0dc61435d · inbound

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries cites this paper.

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:21:09.993373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6984f6f0-9ca8-4d44-b913-0458b2223974 · inbound

Comparing the latent features of universal machine-learning interatomic potentials cites this paper.

Comparing the latent features of universal machine-learning interatomic potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:23:49.420155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e49e7edf-ba72-4760-a39d-88cf69baf13c · inbound

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials cites this paper.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:42.298805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfe6996d-776b-4e68-b656-640447395b68 · inbound

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials cites this paper.

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-29T06:53:12.392166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9ac6222-4313-4f0a-8952-eaa55c79d67c · inbound

SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials cites this paper.

SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T10:06:52.131409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b620855d-3da2-42bd-9acc-3e5347db314c · inbound

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization cites this paper.

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.310203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd7778e1-c381-40e7-8f5f-060e3ba596c5 · inbound

Data-driven atomistic modelling of hybrid halide perovskite passivation cites this paper.

Data-driven atomistic modelling of hybrid halide perovskite passivation Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-07-07T17:24:00.450779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.