Paper Citation Record · LEDGER

High-performance training and inference for deep equivariant interatomic potentials

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2504.16068.

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

pith.paper-citation-record.v1
2504.16068 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:48:00.812931Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 41319b3b-fc24-4cd5-8ad7-c2bbc90a89c3 · inbound

Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes cites this paper.

Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes High-performance training and inference for deep equivariant interatomic potentials

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T23:48:00.812931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeab3395-734a-4455-bd95-6a8f4bd1d3ec · inbound

Physics-Constrained Self-Energy Warm Starts for Charge-Self-Consistent DFT+DMFT: Application to Iron at Core Conditions cites this paper.

Physics-Constrained Self-Energy Warm Starts for Charge-Self-Consistent DFT+DMFT: Application to Iron at Core Conditions High-performance training and inference for deep equivariant interatomic potentials

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:04:16.184813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1253b2a-3736-4398-ae43-a0bcfbc62ec8 · inbound

Accelerating point defect simulations using data-driven and machine learning approaches cites this paper.

Accelerating point defect simulations using data-driven and machine learning approaches High-performance training and inference for deep equivariant interatomic potentials

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:05.199199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7be39d3a-5583-4b84-8b0f-763f38fc2b65 · inbound

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning cites this paper.

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning High-performance training and inference for deep equivariant interatomic potentials

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.090555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75d6fbaa-c0b3-4c5a-8e8a-c8da1c9d085f · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials High-performance training and inference for deep equivariant interatomic potentials

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-19T23:52:52.986072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a88086b0-a17d-4513-8358-fcdb9b3db30d · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials High-performance training and inference for deep equivariant interatomic potentials

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:38:10.420297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72dfa6d7-f0cc-4ca7-9852-934325230acc · inbound

Anomalous Subsurface Vacancy Stabilization Dictated by Geometry-Electronic Decoupling on Metal Surfaces cites this paper.

Anomalous Subsurface Vacancy Stabilization Dictated by Geometry-Electronic Decoupling on Metal Surfaces High-performance training and inference for deep equivariant interatomic potentials

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:13:59.078446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2fd8273f-089d-49ca-8c73-153595d1992d · inbound

Using graph neural networks to predict many-body interactions in amorphous materials cites this paper.

Using graph neural networks to predict many-body interactions in amorphous materials High-performance training and inference for deep equivariant interatomic potentials

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:32:37.367090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b615bc21-33c0-4a7e-9952-f140c948ae3a · inbound

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

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models High-performance training and inference for deep equivariant interatomic potentials

Reference 240

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.027367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab7f6c2d-6d62-4885-b2d2-74a6ebb56765 · inbound

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning cites this paper.

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning High-performance training and inference for deep equivariant interatomic potentials

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:49:03.264092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be17adeb-0c71-4b0a-a38b-3ae4fd8f530e · 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 High-performance training and inference for deep equivariant interatomic potentials

Reference 45

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.