Paper Citation Record · LEDGER

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials

As of 20 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.10002.

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

pith.paper-citation-record.v1
2607.10002 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T01:07:35.895422Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact7
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2c62e41-8924-4fb2-ad7e-e647bff7fd8b · outbound

This paper cites Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set

Reference 1

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Observation 7f49bb84-92ab-4aa1-bcfa-d946f71570b8 · outbound

This paper cites Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 1dc469f1-c12a-454d-bdab-2763f65b6aab · outbound

This paper cites Neural Network Potentials: A Concise Overview of Methods.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Neural Network Potentials: A Concise Overview of Methods

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 9c4d58ce-5112-4259-99e7-9e8ce57773e8 · outbound

This paper cites CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 1861ace7-a47d-43b8-9f0c-14eb451f13ab · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 5aa8e2b3-53ce-4385-98ef-c9c3c6550090 · outbound

This paper cites Wood et al.UMA: A Family of Universal Models for Atoms.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Wood et al.UMA: A Family of Universal Models for Atoms

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 8ced6364-5e05-47b4-b1bc-6643363536aa · outbound

This paper cites Orb-v3: atomistic simulation at scale.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Orb-v3: atomistic simulation at scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 5c3bef60-e702-44b3-b834-068b863149b7 · outbound

This paper cites A review of advancements in coarse-grained molecular dynamics simulations.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials A review of advancements in coarse-grained molecular dynamics simulations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation f936c069-a868-429b-97e6-07260a65204f · outbound

This paper cites Perspective: Coarse-grained models for biomolecular systems.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Perspective: Coarse-grained models for biomolecular systems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 6edafbb9-b4bb-4b33-bdc5-15f0d828c781 · outbound

This paper cites Two decades ofMartini: Better beads, broader scope.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Two decades ofMartini: Better beads, broader scope

Reference 10

Resolution: verified exact; doi, observed 2026-07-14T01:10:07.276679Z

Source-reported events for the cited work

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Observation e9551c2a-542e-4cc9-a48f-4556d2a847d4 · outbound

This paper cites Perspective: Dissipative Particle Dynamics.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Perspective: Dissipative Particle Dynamics

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 05d47916-9b4e-4fdd-8cf3-0f1d9008dd28 · outbound

This paper cites Modification of the overlap potential to mimic a linear site–site potential.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Modification of the overlap potential to mimic a linear site–site potential

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Unavailable: canonical work link unavailable.

Observation 9d1a5117-11cb-417d-a17c-c597e6a790ff · outbound

This paper cites Extension and generalization of the Gay-Berne potential.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Extension and generalization of the Gay-Berne potential

Reference 13

Resolution: verified exact; doi, observed 2026-07-14T01:10:07.286812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f089454-dbce-42a3-84ed-8f7b9cb309d7 · outbound

This paper cites Generalizedneural-networkrepresentationofhigh-dimensional potential-energy surfaces.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Generalizedneural-networkrepresentationofhigh-dimensional potential-energy surfaces

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation a1a5717d-2010-4dd7-9866-c6fdbc5c21b8 · outbound

This paper cites Gaussian approximation potentials: The accuracy of quantum me- chanics, without the electrons.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Gaussian approximation potentials: The accuracy of quantum me- chanics, without the electrons

Reference 15

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Observation 4a05cec9-0e57-4065-8b2f-d01bc5e510c4 · outbound

This paper cites E(3)-equivariant graph neural networks for data-efficient and accu- rate interatomic potentials.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials E(3)-equivariant graph neural networks for data-efficient and accu- rate interatomic potentials

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-07-14T01:07:35.895422Z

Source-reported events for the cited work

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Observation 25d89b3d-da82-458d-bf32-bcabd808cfe9 · outbound

This paper cites Approaching coupled cluster accuracy with a general-purpose neural network potential through active learning.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Approaching coupled cluster accuracy with a general-purpose neural network potential through active learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

Source-reported events for the cited work

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Observation 55797153-8714-47c5-b93c-cee7e7603f6d · outbound

This paper cites Uncertainty-aware dynamics for machine learning interatomic potentials.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Uncertainty-aware dynamics for machine learning interatomic potentials

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 4d3e7c3a-d808-4dbb-9c91-b7148a786175 · outbound

This paper cites Machine learning for coarse-grained molecular simulation: a survey of methods, models, and applications.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Machine learning for coarse-grained molecular simulation: a survey of methods, models, and applications

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-14T01:10:07.261974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9f08ba8-e9c9-4f87-baa8-fee7270f87ae · outbound

This paper cites DeePCG: Constructing coarse-grained models via deep neural networks.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials DeePCG: Constructing coarse-grained models via deep neural networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation da766d67-e556-4cfb-8a3d-bca9c3405ffc · outbound

This paper cites Coarse-graining molecular dynamics with graph neural networks.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Coarse-graining molecular dynamics with graph neural networks

Reference 21

Resolution: verified exact; doi, observed 2026-07-14T01:10:07.252163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 166f897b-42fc-4d14-b02b-3d27c7613f4d · outbound

This paper cites Active learning a coarse-grained neural network model for bulk water from sparse training data.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Active learning a coarse-grained neural network model for bulk water from sparse training data

Reference 22

Resolution: verified exact; doi, observed 2026-07-14T01:10:07.295275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6bb6559-2050-449a-b405-198227ed9922 · outbound

This paper cites On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation a1da38e6-7407-4fc7-b84d-e1e78e6f03d8 · outbound

This paper cites Uncertainty Driven Active Learning of Coarse Grained Free Energy Models.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Uncertainty Driven Active Learning of Coarse Grained Free Energy Models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-14T01:10:07.244882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cfaef853-4b7e-472e-84da-9a7ed806d542 · outbound

This paper cites Deep Potential Molecular Dynamics: A Scalable Model with the Ac- curacy of Quantum Mechanics.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Deep Potential Molecular Dynamics: A Scalable Model with the Ac- curacy of Quantum Mechanics

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation cb0cd364-3acf-42c2-af8f-4c7bfae3c0e1 · outbound

This paper cites Learning local equivariant representations for large-scale atomistic dynamics.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Learning local equivariant representations for large-scale atomistic dynamics

Reference 26

Resolution: malformed identifier; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 0eaaf120-ace4-446f-bb8b-fdcd413db24c · outbound

This paper cites Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-14T01:10:07.261431Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fb0741c8-1b7d-4c4e-993b-77772d4c2fb5 · outbound

This paper cites Anisotropic molecular coarse-graining by force and torque matching with neural networks.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials Anisotropic molecular coarse-graining by force and torque matching with neural networks

Reference 28

Resolution: malformed identifier; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation 65fb4fe1-cffb-407e-a635-661b85625870 · outbound

This paper cites The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Observation cdc0861b-7728-4272-95f3-835e54973e0a · outbound

This paper cites LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T01:07:35.895422Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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