Paper Citation Record · LEDGER

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2605.25866.

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

pith.paper-citation-record.v1
2605.25866 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:55:26.864364Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

21 of 21 outbound references displayed

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External citation measurements

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Outbound references

Observation 4c54dbf7-dcef-4f56-b8a5-ab929bc14916 · outbound

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

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 5bbe512e-accc-4b18-acda-db70ced606d1 · outbound

This paper cites Crysgnn: Distilling pre- trained knowledge to enhance property prediction for crystalline mate- rials.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Crysgnn: Distilling pre- trained knowledge to enhance property prediction for crystalline mate- rials

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 38fecb1b-b6d8-4c03-89e2-4be801bb596f · outbound

This paper cites Physics-guided dual self- supervised learning for structure-based material property prediction.The Journal of Physical Chemistry Letters, 15(10):2841–2850, 2024.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Physics-guided dual self- supervised learning for structure-based material property prediction.The Journal of Physical Chemistry Letters, 15(10):2841–2850, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 1fd80782-0a8d-43a1-98a1-188a150ca4c6 · outbound

This paper cites The emergence of perovskite solar cells.Nature photonics, 8(7):506–514, 2014.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction The emergence of perovskite solar cells.Nature photonics, 8(7):506–514, 2014

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 05dc45de-f8b7-4ecc-b74f-5415e8ba00ff · outbound

This paper cites Hautier, G.and Jain and S.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Hautier, G.and Jain and S

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 4383633f-4178-4ed0-a825-101fc310f09f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Adam: A Method for Stochastic Optimization

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:14:04.680808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 78f6965c-cc5e-488d-8dcb-e0d765924b80 · outbound

This paper cites Efficient approximations of complete interatomic potentials for crystal property prediction.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Efficient approximations of complete interatomic potentials for crystal property prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation afbaf388-b223-4099-825f-68cf6bc4024f · 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.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Graph convolutional neural networks with global attention for improved materials property prediction.Physical Chemistry Chemical Physics, 22(32):18141–18148, 2020

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 7c49ad78-f2e6-4982-a60d-ebf6dc7a8046 · outbound

This paper cites Crysatom: Distributed representation of atoms for crystal property prediction.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Crysatom: Distributed representation of atoms for crystal property prediction

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Unavailable: canonical work link unavailable.

Observation a0716698-0595-4c9f-8b17-fe32d22dbebc · outbound

This paper cites Towards the computational design of solid catalysts.Nature chemistry, 1(1):37–46, 2009.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Towards the computational design of solid catalysts.Nature chemistry, 1(1):37–46, 2009

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation d1a87305-6e59-474d-b7c4-ea22084e3a1b · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Representation Learning with Contrastive Predictive Coding

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:14:04.683199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb59afbc-79b7-46cf-8646-79244da7d5c6 · outbound

This paper cites Jacob’s ladder of density functional approximations for the exchange-correlation energy.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Jacob’s ladder of density functional approximations for the exchange-correlation energy

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation d396e592-4e07-4b9f-8609-c4aebe43f709 · outbound

This paper cites Machine learning in materials informat- ics: recent applications and prospects.npj Computational Materials, 3(1):54, 2017.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Machine learning in materials informat- ics: recent applications and prospects.npj Computational Materials, 3(1):54, 2017

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 2f2a3a7d-2bb1-4552-a8e0-e69bff300bfe · outbound

This paper cites PRISM: Periodic representation with multiscale and similarity graph modelling for enhanced crystal structure property prediction.npj Computational Materials, 2026.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction PRISM: Periodic representation with multiscale and similarity graph modelling for enhanced crystal structure property prediction.npj Computational Materials, 2026

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 87ae4211-8b92-43f3-9d3b-b123e01ddbc2 · outbound

This paper cites A cartesian encoding graph neural network for crystal structure property prediction: application to thermal ellipsoid estimation.Digital Discovery, 4:694– 710, 2025.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction A cartesian encoding graph neural network for crystal structure property prediction: application to thermal ellipsoid estimation.Digital Discovery, 4:694– 710, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation a2fb44a7-ba55-475a-b195-812b3d4b59eb · outbound

This paper cites Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation e24fbeb0-c692-463e-b55b-f1507ea4c9cb · outbound

This paper cites Velickovic, W.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Velickovic, W

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 871970fd-c41d-4813-af32-1cf07bd08cf7 · outbound

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

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Crystal graph convolutional neural networks for an accurate and interpretable prediction of material prop- erties.Physical review letters, 120(14):145301, 2018

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation c40b5df3-32eb-4d54-aa30-2940b5cb8d13 · outbound

This paper cites an unresolved cited work.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation aab559d2-7fda-409e-bffa-6d0402e9dba5 · outbound

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

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Periodic graph transformers for crystal material property prediction.Advances in Neural Information Processing Systems, 35:15066–15080, 2022

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Observation 94ca2f1f-73ad-46c5-afe7-5f42adf2dcdd · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

UNATE: UNsupervised ATomic Embedding for crystal structures property prediction Barlow twins: Self-supervised learning via redundancy reduction

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T22:55:26.864364Z

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Pith citing papers

No inbound Pith citation observations are available.