Paper Citation Record · LEDGER

Crystal Hypergraph Convolutional Networks

As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2411.12616.

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

pith.paper-citation-record.v1
2411.12616 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:23:37.456135Z

measured 47 of 47 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:23:15.435786Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e181196c-1b75-408f-a982-f6f2c2dd5e0e · outbound

This paper cites A general-purpose ma- chine learning framework for predicting prop- erties of inorganic materials.

Crystal Hypergraph Convolutional Networks A general-purpose ma- chine learning framework for predicting prop- erties of inorganic materials

Reference 1

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Observation 4f4411a9-ef83-434e-9131-a0d27ef26a08 · outbound

This paper cites Machine learning in materials science.

Crystal Hypergraph Convolutional Networks Machine learning in materials science

Reference 2

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Observation 21d5ffb7-2123-454e-9577-24651ad80ea0 · outbound

This paper cites Machine learning approaches for the predic- tion of materials properties.

Crystal Hypergraph Convolutional Networks Machine learning approaches for the predic- tion of materials properties

Reference 3

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Observation c0359d7c-7766-4e49-a5ac-3b55adb6e912 · outbound

This paper cites Machine learning in mate- rials genome initiative: A review.

Crystal Hypergraph Convolutional Networks Machine learning in mate- rials genome initiative: A review

Reference 4

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Observation 53950d55-bb7c-40b6-a580-6480a35429f7 · outbound

This paper cites Application of machine learning for advanced material prediction and design.

Crystal Hypergraph Convolutional Networks Application of machine learning for advanced material prediction and design

Reference 5

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Observation 4d79f8c0-91c1-4424-be7b-07ca6694f205 · outbound

This paper cites Graph neural net- works for materials science and chemistry.

Crystal Hypergraph Convolutional Networks Graph neural net- works for materials science and chemistry

Reference 6

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Observation 6a46bf91-32a7-4721-97ea-10610e91cf2b · outbound

This paper cites Machine learning– assisted design of material properties.

Crystal Hypergraph Convolutional Networks Machine learning– assisted design of material properties

Reference 7

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Observation 7933c28e-1065-4172-bd1e-a66d3e8cfd17 · outbound

This paper cites Scope of ma- chine learning in materials research—A re- view.

Crystal Hypergraph Convolutional Networks Scope of ma- chine learning in materials research—A re- view

Reference 8

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Observation 6a9a4ea2-ea17-406d-9fb9-fe7d89b13cc9 · outbound

This paper cites Schnet: A continuous- filter convolutional neural network for mod- eling quantum interactions.

Crystal Hypergraph Convolutional Networks Schnet: A continuous- filter convolutional neural network for mod- eling quantum interactions

Reference 9

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Observation 4a07f00c-0f59-45e8-9d7f-63e4ca0cdf06 · outbound

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

Crystal Hypergraph Convolutional Networks Crystal graph convolutional neural networks for an accurate and interpretable prediction of ma- terial properties

Reference 10

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Observation 97d60a81-5e96-4c0d-9e66-a2ef9068996e · outbound

This paper cites Graph networks as a univer- sal machine learning framework for molecules and crystals.

Crystal Hypergraph Convolutional Networks Graph networks as a univer- sal machine learning framework for molecules and crystals

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:39.051829Z

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Observation 1da01667-48b2-4d8e-81bb-9f1bd7cab1a4 · outbound

This paper cites Chemical Environment Adaptive Learning for Optical Band Gap Prediction of Doped Graphitic Carbon Ni- tride Nanosheets.

Crystal Hypergraph Convolutional Networks Chemical Environment Adaptive Learning for Optical Band Gap Prediction of Doped Graphitic Carbon Ni- tride Nanosheets

Reference 12

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Observation 206992b3-afcf-4122-94da-3cb9470a8b1b · outbound

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

Crystal Hypergraph Convolutional Networks A geometric-information-enhanced crystal graph network for predicting proper- ties of materials

Reference 13

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Observation 59092283-e554-471a-bd90-df4d90c5e93e · outbound

This paper cites De- veloping an improved crystal graph convolu- tional neural network framework for acceler- ated materials discovery.

Crystal Hypergraph Convolutional Networks De- veloping an improved crystal graph convolu- tional neural network framework for acceler- ated materials discovery

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.942449Z

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Observation 10aae8c9-a0a3-4a37-b929-dcb140770e27 · outbound

This paper cites Neural message pass- ing for quantum chemistry.

Crystal Hypergraph Convolutional Networks Neural message pass- ing for quantum chemistry

Reference 15

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Observation 487eecc5-8c58-4a75-8a52-921e8e387d6f · outbound

This paper cites Connectivity optimized nested line graph networks for crystal struc- tures.

Crystal Hypergraph Convolutional Networks Connectivity optimized nested line graph networks for crystal struc- tures

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.890033Z

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Observation 47efa389-5351-4ee0-b819-d7eb340f274f · outbound

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

Crystal Hypergraph Convolutional Networks Atomistic Line Graph Neural Network for improved materials property predictions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.775823Z

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Observation 5b041b4f-c83f-44a1-89fc-a99bbab2d49c · outbound

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

Crystal Hypergraph Convolutional Networks A universal graph deep learning interatomic potential for the periodic table

Reference 18

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Observation 32db0d6f-6ec6-457d-ac0a-7e5b7145c2cf · outbound

This paper cites THE PRINCIPLES DETER- MINING THE STRUCTURE OF COM- PLEX IONIC CRYSTALS.

Crystal Hypergraph Convolutional Networks THE PRINCIPLES DETER- MINING THE STRUCTURE OF COM- PLEX IONIC CRYSTALS

Reference 19

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Observation d5f17e81-bb81-400b-a949-c8fc3b62a0a5 · outbound

This paper cites The shapes of coordination poly- hedra.

Crystal Hypergraph Convolutional Networks The shapes of coordination poly- hedra

Reference 20

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Observation 7d04ea7e-4e3f-42a9-baa0-5d0c8b25c9e2 · outbound

This paper cites Statistical analy- sis of coordination environments in oxides.

Crystal Hypergraph Convolutional Networks Statistical analy- sis of coordination environments in oxides

Reference 21

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Observation b92a736a-91ba-4d72-b46c-71f1fa2529fb · outbound

This paper cites Exploring Motifs and Their Hierarchies in Crystals via Unsuper- vised Learning.

Crystal Hypergraph Convolutional Networks Exploring Motifs and Their Hierarchies in Crystals via Unsuper- vised Learning

Reference 22

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Observation ca8cbb33-5260-4507-90e6-2288b338b3cc · outbound

This paper cites Coordination motifs and large-scale structural organiza- tion in atomic clusters.

Crystal Hypergraph Convolutional Networks Coordination motifs and large-scale structural organiza- tion in atomic clusters

Reference 23

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Observation 970832d3-075c-4d45-a4be-2261d68aa98b · outbound

This paper cites Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks.

Crystal Hypergraph Convolutional Networks Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

Reference 24

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This paper cites Assessing Lo- cal Structure Motifs Using Order Parame- ters for Motif Recognition, Interstitial Iden- tification, and Diffusion Path Characteriza- tion.

Crystal Hypergraph Convolutional Networks Assessing Lo- cal Structure Motifs Using Order Parame- ters for Motif Recognition, Interstitial Iden- tification, and Diffusion Path Characteriza- tion

Reference 25

Resolution: verified exact; doi, observed 2026-08-12T17:23:37.618571Z

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Observation 0a64509f-e832-4e89-87d4-a447ed74e620 · outbound

This paper cites Local structure order parameters and site fingerprints for quantification of coordina- tion environment and crystal structure sim- ilarity.

Crystal Hypergraph Convolutional Networks Local structure order parameters and site fingerprints for quantification of coordina- tion environment and crystal structure sim- ilarity

Reference 26

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Observation e2ede2d5-09dc-48a5-b3b5-6d8d9025c70d · outbound

This paper cites A general set of order parameters for molecular crystals.

Crystal Hypergraph Convolutional Networks A general set of order parameters for molecular crystals

Reference 27

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Observation 0fa10aad-08e3-4f02-bb35-d756ccdcfc15 · outbound

This paper cites CLEASE: a versatile and user-friendly implementation of cluster expansion method.

Crystal Hypergraph Convolutional Networks CLEASE: a versatile and user-friendly implementation of cluster expansion method

Reference 28

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Observation ef3945c8-7a06-480f-b925-88e5b80e6705 · outbound

This paper cites A complete representation of structure–property relationships in crys- tals.

Crystal Hypergraph Convolutional Networks A complete representation of structure–property relationships in crys- tals

Reference 29

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Observation 563ba66a-0410-4746-9609-f288bdbd5f12 · outbound

This paper cites Generalized cluster description of multicom- ponent systems.

Crystal Hypergraph Convolutional Networks Generalized cluster description of multicom- ponent systems

Reference 30

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Observation f1c551a0-b64c-4d84-acae-f4426976f059 · outbound

This paper cites Continuous symmetry measures. 5. The classical polyhe- dra.

Crystal Hypergraph Convolutional Networks Continuous symmetry measures. 5. The classical polyhe- dra

Reference 31

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Observation 6fe3af2f-6131-43f3-8c5d-e557af3be638 · outbound

This paper cites ChemEnv: a fast and robust coordination environment identi- fication tool.

Crystal Hypergraph Convolutional Networks ChemEnv: a fast and robust coordination environment identi- fication tool

Reference 32

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Observation 5e2df41a-90c5-4081-bb34-343a53d4b285 · outbound

This paper cites Benchmarking Coordina- tion Number Prediction Algorithms on In- organic Crystal Structures.

Crystal Hypergraph Convolutional Networks Benchmarking Coordina- tion Number Prediction Algorithms on In- organic Crystal Structures

Reference 33

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Observation cd3c94d8-0c22-41b6-beb8-cee9e8f4888c · outbound

This paper cites Supervised Community Detection with Line Graph Neural Networks.

Crystal Hypergraph Convolutional Networks Supervised Community Detection with Line Graph Neural Networks

Reference 34

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Observation 9006592d-689d-4c8f-bb98-1eaea620bb86 · outbound

This paper cites Commentary: The Materials Project: A materials genome ap- proach to accelerating materials innovation.

Crystal Hypergraph Convolutional Networks Commentary: The Materials Project: A materials genome ap- proach to accelerating materials innovation

Reference 35

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This paper cites Benchmarking mate- rials property prediction methods: the Mat- bench test set and Automatminer reference algorithm.

Crystal Hypergraph Convolutional Networks Benchmarking mate- rials property prediction methods: the Mat- bench test set and Automatminer reference algorithm

Reference 36

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Observation 37da0968-8eb3-4e4e-aa3e-b6a9eb66dbc9 · outbound

This paper cites Structure motif– centric learning framework for inorganic crys- talline systems.

Crystal Hypergraph Convolutional Networks Structure motif– centric learning framework for inorganic crys- talline systems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.101782Z

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Observation 349458b8-989c-4efe-a6b3-dfb9169ac895 · outbound

This paper cites Motif-Driven Contrastive Learning of Graph Representations.

Crystal Hypergraph Convolutional Networks Motif-Driven Contrastive Learning of Graph Representations

Reference 38

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This paper cites Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks.

Crystal Hypergraph Convolutional Networks Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:23:37.775140Z

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Observation 406dd253-43e7-4743-9d06-a9010cc0a582 · outbound

This paper cites Constant size descriptors for accurate machine learning models of molecular properties.

Crystal Hypergraph Convolutional Networks Constant size descriptors for accurate machine learning models of molecular properties

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.077686Z

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Observation 041bbb91-5102-4df8-8ce2-23d898105d20 · outbound

This paper cites Extending machine learn- ing beyond interatomic potentials for predict- ing molecular properties.

Crystal Hypergraph Convolutional Networks Extending machine learn- ing beyond interatomic potentials for predict- ing molecular properties

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:38.054647Z

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Observation 7f78b356-14d8-49ab-bc98-a44eec8b91c1 · outbound

This paper cites e3nn: Euclidean Neural Networks.

Crystal Hypergraph Convolutional Networks e3nn: Euclidean Neural Networks

Reference 42

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

Crystal Hypergraph Convolutional Networks Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 43

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Observation a0e36db1-af25-46c4-9469-f5f0c1a184ed · outbound

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

Crystal Hypergraph Convolutional Networks Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Reference 44

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This paper cites Hypergraph convolution and hypergraph at- tention.

Crystal Hypergraph Convolutional Networks Hypergraph convolution and hypergraph at- tention

Reference 45

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Observation ccbd75a5-bc9b-49e3-8503-a440d831a184 · outbound

This paper cites net / forum?id=H1g0Z3A9Fm.

Crystal Hypergraph Convolutional Networks net / forum?id=H1g0Z3A9Fm

Reference 2019

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Pith citing papers

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

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

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

Reference 20

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