Paper Citation Record · LEDGER

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2503.14118.

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

pith.paper-citation-record.v1
2503.14118 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:13:00.704158Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:38:19.646875Z

Reference resolution

0 of 0 outbound references displayed

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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 ccca1166-97e7-418f-9096-747863a9ad47 · inbound

Machine Learning the Energetics of Electrified Solid/Liquid Interfaces cites this paper.

Machine Learning the Energetics of Electrified Solid/Liquid Interfaces PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:00.704158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60468bf3-c455-4a5f-9213-fd6a60af051c · inbound

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

Distillation of atomistic foundation models across architectures and chemical domains PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:22.240365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71c05811-b63c-458d-bc6e-76e7c421fb02 · inbound

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure cites this paper.

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:56.804916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 669b783c-18ef-4755-a1bd-947e2616e845 · inbound

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

AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:11:40.371941Z

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 4bb65a46-7455-4b45-af82-07b94d0db898 · inbound

Simultaneous Learning of Static and Dynamic Charges cites this paper.

Simultaneous Learning of Static and Dynamic Charges PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:54:16.436041Z

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 35a2cda3-a5b5-4ed6-a6af-d54608d485ab · 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 PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:10.578898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 125ff0a4-defa-4dd2-9d14-15bd116ae5fc · 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 PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T10:06:52.118436Z

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 9d3ac425-546d-4882-892f-d33a2a6e3d55 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.152793Z

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 92344e64-1bb2-402d-830c-ef2631824814 · inbound

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

Fine-tuning MLIP foundation models: strategies for accuracy and transferability PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:38:19.648277Z

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 8dae38fa-4ea5-432a-b03d-3de1d355cf37 · inbound

Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery cites this paper.

Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-12T11:08:03.688246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.