Paper Citation Record · LEDGER

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.24818.

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pith.paper-citation-record.v1
2607.24818 v1

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T22:23:17.856830Z

measured 36 of 36 standing notices

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Reference resolution

36 of 36 outbound references displayed

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Outbound references

Observation 0905117d-0f46-47bc-baac-118c404db61a · outbound

This paper cites Quantifying uncertainty in high- throughput density functional theory: A comparison of aflow, materials project, and oqmd.Physical Review Materials, 7(5):053805, 2023.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Quantifying uncertainty in high- throughput density functional theory: A comparison of aflow, materials project, and oqmd.Physical Review Materials, 7(5):053805, 2023

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:12.667274Z

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Observation 0428a779-650b-4900-b1be-a323804dbac2 · outbound

This paper cites Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022

Reference 2

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Observation 5bd8c4b8-ab32-480d-9c69-6ec925d8cb47 · outbound

This paper cites Graph convolutional neural networks with global attention for improved materials property prediction.Physical Chemistry Chemical Physics, 22(32):18141–18148, 2020.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Graph convolutional neural networks with global attention for improved materials property prediction.Physical Chemistry Chemical Physics, 22(32):18141–18148, 2020

Reference 3

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Observation 4706678a-3cbf-45e1-9a4f-4a33748d3030 · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018

Reference 4

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Observation 17d26571-f4f8-4cd4-8fd5-a9b069f0cb62 · outbound

This paper cites Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7(1):185, 2021.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7(1):185, 2021

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:13.215408Z

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Observation 5238aa95-3854-4e0e-9f3a-c223781f44b3 · outbound

This paper cites Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:13.383372Z

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Observation 21e38f66-e492-407d-b66f-e9c0568cee38 · outbound

This paper cites Periodic graph transformers for crystal material property prediction.Advances in Neural Information Processing Systems, 35:15066–15080, 2022.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Periodic graph transformers for crystal material property prediction.Advances in Neural Information Processing Systems, 35:15066–15080, 2022

Reference 7

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Observation 27e4db33-803f-4d82-86e5-7ed633a803c1 · outbound

This paper cites ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 8

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Observation 5cdf357f-3361-4adb-84bd-49b30b6dc0af · outbound

This paper cites Pscg-net: A multiscale crystal graph neural network for accelerated materials discovery.Journal of Chemical Information and Modeling, 65(20):10871–10884, 2025.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Pscg-net: A multiscale crystal graph neural network for accelerated materials discovery.Journal of Chemical Information and Modeling, 65(20):10871–10884, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:13.757997Z

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Observation 1984069d-997f-4a60-aad1-60e4cd4cbacf · outbound

This paper cites From polyhedra to crystals: a graph- theoretic framework for crystal structure generation.CrystEngComm, 2026.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks From polyhedra to crystals: a graph- theoretic framework for crystal structure generation.CrystEngComm, 2026

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:13.875440Z

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Observation 91fe529f-0fc2-408b-96ae-0319b567268e · outbound

This paper cites Local coordina- tion versus overall topology in crystal structures: deriving knowledge from crystallographic databases.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Local coordina- tion versus overall topology in crystal structures: deriving knowledge from crystallographic databases

Reference 11

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Observation 0a239ce4-02a2-4386-81e2-7020800e638e · outbound

This paper cites Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL Materials, 1:011002, 2013.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL Materials, 1:011002, 2013

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:14.153191Z

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Observation b641597e-21cc-4924-aaa6-6152e21174c6 · outbound

This paper cites Schnet– a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Schnet– a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:14.309302Z

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Observation 2932ef01-7b80-4707-9db2-9073ab416063 · outbound

This paper cites Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.Science Advances, 9:eadi3245, 2023.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.Science Advances, 9:eadi3245, 2023

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:14.520539Z

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Observation 9cfed336-78a0-4f2a-a69a-642f78038505 · outbound

This paper cites Garrity, Andrew C.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Garrity, Andrew C

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:14.607277Z

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Observation 1a61b0b8-7e86-4c4c-8726-03f7ee263d63 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:14.792990Z

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Observation 0e7dae93-747a-4753-98ac-6d3a80492c1f · outbound

This paper cites Fast and uncertainty-aware directional message passing for non-equilibrium molecules.NeurIPS 2020 Workshop on Machine Learning for Molecules, 2020.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Fast and uncertainty-aware directional message passing for non-equilibrium molecules.NeurIPS 2020 Workshop on Machine Learning for Molecules, 2020

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.029306Z

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Observation 1c23b5a9-ffaa-493c-be12-76cd88882771 · outbound

This paper cites Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802, 2021.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802, 2021

Reference 18

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Observation 6c104bf3-6d11-4900-8f51-ba3afbe009b2 · outbound

This paper cites Spherical message passing for 3d molecular graphs.International Conference on Learning Representations (ICLR), 2022.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Spherical message passing for 3d molecular graphs.International Conference on Learning Representations (ICLR), 2022

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.353400Z

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Observation f2fc28d9-9a1b-43ce-b88b-cc5224f87047 · outbound

This paper cites Crystal Hypergraph Convolutional Networks.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Crystal Hypergraph Convolutional Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.435786Z

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Observation 6d045252-b423-4363-bf38-804c9e40c184 · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table.Nature Computational Science, 2:718–728, 2022.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks A universal graph deep learning interatomic potential for the periodic table.Nature Computational Science, 2:718–728, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.532531Z

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Observation 04342313-ad27-4756-8d36-df44c087cc01 · outbound

This paper cites Bartel, and Gerbrand Ceder.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Bartel, and Gerbrand Ceder

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.716851Z

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Observation 7213fcdb-87d5-4653-be15-443bef83da75 · outbound

This paper cites an unresolved cited work.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:15.835519Z

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Observation 180780a3-a3a8-4f8e-892c-8b1a5fb76e52 · outbound

This paper cites High-throughput identification and characterization of two-dimensional materials using density functional theory.Scientific reports, 7(1):5179, 2017.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks High-throughput identification and characterization of two-dimensional materials using density functional theory.Scientific reports, 7(1):5179, 2017

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.024752Z

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Observation f72738ff-22ab-4446-9ff3-fad6b5c18657 · outbound

This paper cites Computational screening of high-performance optoelectronic materials using optb88vdw and tb-mbj formalisms.Scientific data, 5(1):180082, 2018.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Computational screening of high-performance optoelectronic materials using optb88vdw and tb-mbj formalisms.Scientific data, 5(1):180082, 2018

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.109368Z

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Observation 136b17ce-c05f-4103-be64-c88a6df5bc4e · outbound

This paper cites Elastic properties of bulk and low-dimensional materials using van der waals density functional.Physical review.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Elastic properties of bulk and low-dimensional materials using van der waals density functional.Physical review

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.192933Z

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Observation 71f35636-d355-4636-bea2-94b57897ad2a · outbound

This paper cites Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.370257Z

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Observation 7fd43761-5257-4e51-bc5c-4d4733bc232e · outbound

This paper cites Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape.Physical review materials, 2(8):083801, 2018.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape.Physical review materials, 2(8):083801, 2018

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.563714Z

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Observation b5fbfbb1-a235-4a4b-afe8-f9acf073d486 · outbound

This paper cites High-throughput discovery of topologically non-trivial materials using spin-orbit spillage.Scientific reports, 9(1):8534, 2019.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks High-throughput discovery of topologically non-trivial materials using spin-orbit spillage.Scientific reports, 9(1):8534, 2019

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.683815Z

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Observation 6854da70-f0ac-47bf-acf1-c0fda7a93c34 · outbound

This paper cites The joint automated repository for various integrated simulations (jarvis) for data-driven materials design.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks The joint automated repository for various integrated simulations (jarvis) for data-driven materials design

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:16.879931Z

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Observation 494e4836-15cc-4e40-b6ec-2086f133380b · outbound

This paper cites Prediction errors of molecular machine learning models lower than hybrid dft error.Journal of chemical theory and computation, 13(11):5255–5264, 2017.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Prediction errors of molecular machine learning models lower than hybrid dft error.Journal of chemical theory and computation, 13(11):5255–5264, 2017

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.071834Z

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Observation 8114ef2c-3c10-47fb-a19d-6a1749024393 · outbound

This paper cites Fast and uncertainty-aware directional message passing for non-equilibrium molecules.Machine Learning for Molecules Workshop at NeurIPS, 2020.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Fast and uncertainty-aware directional message passing for non-equilibrium molecules.Machine Learning for Molecules Workshop at NeurIPS, 2020

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.223611Z

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Observation 7d2c5a56-e330-4382-99c7-755bf224c57d · outbound

This paper cites Strategies for pre-training graph neural networks.International Conference on Learning Representa- tions (ICLR), 2020.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Strategies for pre-training graph neural networks.International Conference on Learning Representa- tions (ICLR), 2020

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.399979Z

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Observation 089ec794-b649-40ea-9db7-9649b4ae2a9f · outbound

This paper cites Ssformer: Self-supervised transformer model for predicting the properties of crystalline materials.Computational Materials Science, 263:114447, 2026.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Ssformer: Self-supervised transformer model for predicting the properties of crystalline materials.Computational Materials Science, 263:114447, 2026

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.556563Z

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Observation f0f331b3-f029-412c-b64f-59631affd39e · outbound

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Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.730462Z

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Observation 94d5464a-b6f7-42e0-a5b2-61223ca6d838 · outbound

This paper cites Benchmarking materi- als property prediction methods: the matbench test set and automatminer reference algorithm.npj Computational Materials, 6(1):138, 2020.

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks Benchmarking materi- als property prediction methods: the matbench test set and automatminer reference algorithm.npj Computational Materials, 6(1):138, 2020

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T22:23:17.856830Z

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