Paper Citation Record · LEDGER

Point Group Equivariant Graph Neural Networks for Materials

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.16871.

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

pith.paper-citation-record.v1
2607.16871 v1

Coverage vector

measured 40 of 40 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T19:46:58.840039Z

measured 40 of 40 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

40 of 40 outbound references displayed

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verified fuzzy0
unresolved33
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malformed identifier3
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External citation measurements

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Outbound references

Observation 49350d8d-7717-4d54-a59d-aec17bd31c40 · outbound

This paper cites Machine learning in materials science.

Point Group Equivariant Graph Neural Networks for Materials Machine learning in materials science

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.032450Z

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Observation 969bf214-422c-43e0-8877-8065be06fae6 · outbound

This paper cites Machine learning in materials genome initiative: A review.

Point Group Equivariant Graph Neural Networks for Materials Machine learning in materials genome initiative: A review

Reference 2

Resolution: verified exact; doi, observed 2026-08-01T19:48:26.318009Z

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Observation c0b43d61-eb95-4c33-b14f-16eca1ea5ec8 · outbound

This paper cites Scope of machine learning in materials research—A review.

Point Group Equivariant Graph Neural Networks for Materials Scope of machine learning in materials research—A review

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-01T19:46:55.234280Z

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Observation 3527f86b-db8b-4be0-a488-a9b8ef952d5a · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.

Point Group Equivariant Graph Neural Networks for Materials Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties

Reference 4

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Observation 1ba69d6f-a9a6-41ce-913e-582c98d7a644 · outbound

This paper cites Crystal hypergraph convolutional networks.

Point Group Equivariant Graph Neural Networks for Materials Crystal hypergraph convolutional networks

Reference 5

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Observation 9058e224-e385-4ced-b893-5af09043ad1c · outbound

This paper cites Graph networks as a universal machine learning framework for molecules and crystals.

Point Group Equivariant Graph Neural Networks for Materials Graph networks as a universal machine learning framework for molecules and crystals

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.496593Z

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Observation 1f4aeb02-0029-4dc9-a2f3-d3277b1829d5 · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table.

Point Group Equivariant Graph Neural Networks for Materials A universal graph deep learning interatomic potential for the periodic table

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.615134Z

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Observation 41e4ca22-7fff-4f5f-ad75-9b3179314cb7 · outbound

This paper cites e3nn: Euclidean Neural Networks.

Point Group Equivariant Graph Neural Networks for Materials e3nn: Euclidean Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.714823Z

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Observation 3a35b2ce-4d6f-40e4-a938-e2dbc01dfce9 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

Point Group Equivariant Graph Neural Networks for Materials Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.803663Z

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Observation 87199649-0f5f-43f5-991f-4bb791bacaf1 · outbound

This paper cites Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals.

Point Group Equivariant Graph Neural Networks for Materials Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:55.895081Z

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Observation 75b2c51c-b4a4-49fd-b45a-06c744956979 · outbound

This paper cites Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures.

Point Group Equivariant Graph Neural Networks for Materials Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures

Reference 11

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Observation 5b4f3370-ca94-4df0-8ad2-8a750fe05303 · outbound

This paper cites Equivariance with Learned Canonicalization Functions.

Point Group Equivariant Graph Neural Networks for Materials Equivariance with Learned Canonicalization Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.178448Z

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Observation ffb7aa6d-69ce-4c13-859f-8bef694d4a17 · outbound

This paper cites Spatial Transformer Networks.

Point Group Equivariant Graph Neural Networks for Materials Spatial Transformer Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.249386Z

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Observation 6b0e2b8e-7af7-4829-bae5-0d5d31af96a2 · outbound

This paper cites Equivariant Adaptation of Large Pretrained Models.

Point Group Equivariant Graph Neural Networks for Materials Equivariant Adaptation of Large Pretrained Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.376650Z

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Observation 81e93f0b-82c2-41fc-9871-41bfcab509e6 · outbound

This paper cites Spglib: a software library for crystal symmetry search.

Point Group Equivariant Graph Neural Networks for Materials Spglib: a software library for crystal symmetry search

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.495791Z

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Observation 0770acfb-d3de-430c-81e2-b29a17e28344 · outbound

This paper cites Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction.

Point Group Equivariant Graph Neural Networks for Materials Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.582834Z

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Observation f1eb1f34-80cd-4cd7-9f5a-2e83313936da · outbound

This paper cites SchNet – A deep learning architecture for molecules and materials.

Point Group Equivariant Graph Neural Networks for Materials SchNet – A deep learning architecture for molecules and materials

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.754744Z

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Observation 08d6d9eb-8346-4f8e-b7c0-e9b3f20432ae · outbound

This paper cites Atomistic Line Graph Neural Network for improved materials property predictions.

Point Group Equivariant Graph Neural Networks for Materials Atomistic Line Graph Neural Network for improved materials property predictions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.849878Z

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Observation 50d43292-d4a9-4133-80dc-a978dc031dcc · outbound

This paper cites Developing an improved crystal graph convolutional neu- ral network framework for accelerated materials discovery.

Point Group Equivariant Graph Neural Networks for Materials Developing an improved crystal graph convolutional neu- ral network framework for accelerated materials discovery

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:56.964846Z

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Observation 1f51ffd8-4334-4ce9-8c68-ca1b4745a8a2 · outbound

This paper cites A geometric-information-enhanced crystal graph network for predicting properties of materials.

Point Group Equivariant Graph Neural Networks for Materials A geometric-information-enhanced crystal graph network for predicting properties of materials

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.084753Z

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Observation 37ae46eb-c041-4fef-83c9-46f13cd85683 · outbound

This paper cites SE(3)-Transformers: 3D roto-translation equivariant attention networks.

Point Group Equivariant Graph Neural Networks for Materials SE(3)-Transformers: 3D roto-translation equivariant attention networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.172862Z

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Observation c55d5b5f-4e32-45c3-bc21-608c0d57d947 · outbound

This paper cites Cormorant: Covariant molecular neural net- works.

Point Group Equivariant Graph Neural Networks for Materials Cormorant: Covariant molecular neural net- works

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.284744Z

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Observation 479c3d27-86ee-4e2a-9f41-391f86aaf01c · outbound

This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate inter- atomic potentials.

Point Group Equivariant Graph Neural Networks for Materials E(3)-equivariant graph neural networks for data-efficient and accurate inter- atomic potentials

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.381873Z

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Observation 7ae9784b-80af-480e-af65-e1e44d6d014c · outbound

This paper cites MACE: Higher order equivariant message passing neural networks for fast and accurate force fields.

Point Group Equivariant Graph Neural Networks for Materials MACE: Higher order equivariant message passing neural networks for fast and accurate force fields

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.467399Z

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Observation 9490b14c-db53-40e4-b703-aef25e541218 · outbound

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

Point Group Equivariant Graph Neural Networks for Materials Learning local equivariant representations for large-scale atomistic dynamics

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.566097Z

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Observation ff572c8c-a2a9-4fe8-8dd6-a4ac75cad751 · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

Point Group Equivariant Graph Neural Networks for Materials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.677168Z

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Observation 3140506c-9f78-4d32-8639-68e634999b63 · outbound

This paper cites An equivariant graph neural network for the elasticity tensors of all seven crystal systems.

Point Group Equivariant Graph Neural Networks for Materials An equivariant graph neural network for the elasticity tensors of all seven crystal systems

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.781999Z

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Observation 10af8d9f-caab-4d0d-8ac4-8dc372013323 · outbound

This paper cites Discovery of Highly Anisotropic Dielectric Crystals with Equiv- ariant Graph Neural Networks.

Point Group Equivariant Graph Neural Networks for Materials Discovery of Highly Anisotropic Dielectric Crystals with Equiv- ariant Graph Neural Networks

Reference 28

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Observation 4f8a43a7-381a-43f0-9ab4-4cc29dcf8db0 · outbound

This paper cites Frame averaging for invariant and equivariant network design.

Point Group Equivariant Graph Neural Networks for Materials Frame averaging for invariant and equivariant network design

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:57.925922Z

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Observation 4166c22a-3738-4dce-805e-d2f9d1ef1d01 · outbound

This paper cites Connectivity optimized nested line graph networks for crystal structures.

Point Group Equivariant Graph Neural Networks for Materials Connectivity optimized nested line graph networks for crystal structures

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.010479Z

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Observation ca6b89ba-c25b-47d1-b4b9-0527bf1649e2 · outbound

This paper cites Remarks on Wyckoff positions.

Point Group Equivariant Graph Neural Networks for Materials Remarks on Wyckoff positions

Reference 31

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Observation b997f1a6-cc95-45b7-a126-26421ecaafae · outbound

This paper cites Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis.

Point Group Equivariant Graph Neural Networks for Materials Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.207715Z

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Observation 0511fab1-52f8-4893-9283-2f90fcd50f45 · outbound

This paper cites Neural message passing for quantum chemistry.

Point Group Equivariant Graph Neural Networks for Materials Neural message passing for quantum chemistry

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.315362Z

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Observation 29d423ce-40aa-4641-a313-702486754cd3 · outbound

This paper cites Complete and Efficient Graph Transformers for Crystal Material Property Prediction.

Point Group Equivariant Graph Neural Networks for Materials Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.396390Z

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Observation 4ebb4ddb-5646-4a8a-9706-889be0f18556 · outbound

This paper cites Bradley and Arthur P.

Point Group Equivariant Graph Neural Networks for Materials Bradley and Arthur P

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.480728Z

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Observation a8c16f68-63c8-45fe-b493-9c590d99c5a4 · outbound

This paper cites Multipole expansion for magnetic structures: A generation scheme for a symmetry- adapted orthonormal basis set in the crystallographic point group.

Point Group Equivariant Graph Neural Networks for Materials Multipole expansion for magnetic structures: A generation scheme for a symmetry- adapted orthonormal basis set in the crystallographic point group

Reference 36

Resolution: verified exact; doi, observed 2026-08-01T19:48:25.977493Z

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Observation ee109088-cb6a-47cc-8269-f52db4f21314 · outbound

This paper cites Commentary: The Materials Project: A materials genome approach to accelerating materials innovation.

Point Group Equivariant Graph Neural Networks for Materials Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Reference 37

Resolution: malformed identifier; doi_truncated, observed 2026-08-01T19:48:25.713609Z

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

Observation 4c4ab5d1-db21-4fdc-b415-f1d25f9e2487 · outbound

This paper cites Charting the complete elastic properties of inorganic crystalline compounds.

Point Group Equivariant Graph Neural Networks for Materials Charting the complete elastic properties of inorganic crystalline compounds

Reference 38

Resolution: verified exact; doi, observed 2026-08-01T19:48:25.311655Z

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Observation 34d7ff68-5f0e-43da-abde-268843c63d5f · outbound

This paper cites High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials.

Point Group Equivariant Graph Neural Networks for Materials High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T19:46:58.781915Z

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Observation fe2ebdd1-0d0e-4bce-834f-6dfbd662e530 · outbound

This paper cites A database to enable discovery and design of piezoelectric materials.

Point Group Equivariant Graph Neural Networks for Materials A database to enable discovery and design of piezoelectric materials

Reference 40

Resolution: verified exact; doi, observed 2026-08-01T19:48:25.017338Z

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

Pith citing papers

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