Paper Citation Record · LEDGER

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2202.02541.

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

pith.paper-citation-record.v1
2202.02541 v2

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measured 0 of 0 reference resolution

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measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:43:35.056792Z

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

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External citation measurements

58
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 8987f502-d99d-4caf-938f-de20fd77c94f · inbound

OpenQDC: Open Quantum Data Commons cites this paper.

OpenQDC: Open Quantum Data Commons TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 116

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Observation 75cd6e93-313a-433a-8786-18febef9f0c4 · inbound

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials cites this paper.

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:05.706512Z

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Observation b04e8d1f-8638-45d7-a848-66a324de1747 · inbound

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity cites this paper.

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T16:26:03.497705Z

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Observation ec0c229d-e6a3-4214-978a-b765f94d9ff9 · inbound

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding cites this paper.

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:35.056792Z

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Observation 9b80ca92-9aec-4614-8d5b-438bbdb61b20 · inbound

xChemAgents: Agentic AI for Explainable Quantum Chemistry cites this paper.

xChemAgents: Agentic AI for Explainable Quantum Chemistry TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:58:14.491376Z

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Observation 0bbf6ac5-09fd-4cc9-9bd5-03f720495f2b · inbound

Self-Refining Training for Amortized Density Functional Theory cites this paper.

Self-Refining Training for Amortized Density Functional Theory TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:56:08.972797Z

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Observation 4ad735d3-aa6e-4141-bc8b-15d811047080 · inbound

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools cites this paper.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:59.830841Z

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Observation 18ffb4f9-c99c-45fe-8743-8f3d9882a9a5 · inbound

Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding cites this paper.

Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:05.988286Z

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Observation 17189d98-f164-4f82-9790-607c0f46ee97 · inbound

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics cites this paper.

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T22:36:20.078457Z

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Observation 32d9cfcc-6577-4125-8aec-1683390afaa1 · 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 TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 63

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Observation d045517f-6bf7-49c8-8af2-c1d9165cebf7 · inbound

Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery cites this paper.

Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:20:28.566129Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ed1c96d-8e85-4abc-a19a-c6f9b18baf68 · inbound

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics cites this paper.

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-13T12:05:54.442913Z

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Observation b5848d51-e5d4-4914-94d1-9dcab8abda2d · inbound

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era cites this paper.

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.482839Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e15af648-9302-49ee-ac02-57b67a4b981b · inbound

Quotient-Space Diffusion Models cites this paper.

Quotient-Space Diffusion Models TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:16:30.255743Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 521c769c-4b4e-4b4e-8404-904fc7fbdf9f · inbound

h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network cites this paper.

h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:31:14.328442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 127396f4-3df4-42f3-8be2-6237b698ce2a · inbound

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution cites this paper.

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:26:24.149984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b6d718a-e97b-4d6a-b9f8-01a573c2fc6f · inbound

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming cites this paper.

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:56:56.950113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af626b9d-ba7a-4a9d-8dbf-66f5d1c8586f · inbound

Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning cites this paper.

Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 140

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T19:29:48.244161Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c65b3d1b-8531-46bb-b36d-319cc8c41b63 · inbound

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening cites this paper.

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:09:22.471398Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 409c415f-11d8-4a42-829e-aefc444e51a3 · inbound

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials cites this paper.

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:24:12.764016Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6bd3fabc-7c54-4205-aff7-a4026aa01dac · inbound

Transformer Atomic Cluster Expansion: TRACE cites this paper.

Transformer Atomic Cluster Expansion: TRACE TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T01:54:38.794979Z

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Observation ba3a34ec-7715-4f96-a212-dbc8679530fd · inbound

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations cites this paper.

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 163

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:40.141443Z

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