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Paper Citation Record · LEDGER

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2606.23166.

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

pith.paper-citation-record.v1
2606.23166 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:52:45.749053Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:30:06.284132Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20e2a800-1d99-4ab6-af14-f857a1596be4 · outbound

This paper cites Donti, Lynn H.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Donti, Lynn H

Reference 1

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Observation 3bd08168-93c0-4c6d-9e0e-7750483eab8f · outbound

This paper cites & Rehme, S.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Rehme, S

Reference 2

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Observation d7a3a05b-d4d4-4a5e-83ae-34b96301fadf · outbound

This paper cites W.et al.Computational screening of all stoichiometric inorganic mate- rials.Chem1, 617–627 (2016).

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials W.et al.Computational screening of all stoichiometric inorganic mate- rials.Chem1, 617–627 (2016)

Reference 3

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Observation 4c4f6a6c-44a8-4e12-93e7-734f49bbf232 · outbound

This paper cites & Walsh, A.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Walsh, A

Reference 4

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Observation d217ca79-c159-4a08-9762-a7dd1301d64d · outbound

This paper cites AI-driven materials design: a mini-review.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials AI-driven materials design: a mini-review

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 64462fb9-31bb-4e06-967d-7531ce29e02e · outbound

This paper cites \ De Breuck , author H.-C.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials \ De Breuck , author H.-C

Reference 6

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 7a6af7c1-89eb-4345-8a4b-23b6cc7e195c · outbound

This paper cites Materials generation in the era of artificial intelligence: A comprehensive survey.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Materials generation in the era of artificial intelligence: A comprehensive survey

Reference 7

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Observation 7855338e-69b6-43b4-94a5-bc942a7b9e55 · outbound

This paper cites Generative design of inorganic materials.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Generative design of inorganic materials

Reference 8

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Unresolved cited work

Reference 9

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Observation 93ef9f5a-9618-4b96-afad-332ed0083ea4 · outbound

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Unresolved cited work

Reference 10

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Observation 846a5fcf-cc71-42f3-8685-2b351ac94f75 · outbound

This paper cites W., Oganov, A.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials W., Oganov, A

Reference 11

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Observation 42f018e8-b541-4cda-a6e7-1de6fe3a90ea · outbound

This paper cites & Zurek, E.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Zurek, E

Reference 12

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Unresolved cited work

Reference 13

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Observation 11bdada3-13b9-400b-893f-e1ef21fea41b · outbound

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Unresolved cited work

Reference 14

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Observation 65b018ff-d4d4-4e6c-b858-ca3ec3b83416 · outbound

This paper cites C., Tibbetts, K.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials C., Tibbetts, K

Reference 15

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This paper cites & Ceder, G.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Ceder, G

Reference 16

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Unresolved cited work

Reference 17

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials npj Comput

Reference 18

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This paper cites URL https: //doi.org/10.1038/s41524-024-01471-8.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials URL https: //doi.org/10.1038/s41524-024-01471-8

Reference 19

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Source-reported events for the cited work

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Reference 20

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This paper cites M., Pulido, A., Kurlin, V.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials M., Pulido, A., Kurlin, V

Reference 21

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Marques, M

Reference 22

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This paper cites BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling

Reference 23

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Jaakkola, T

Reference 24

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This paper cites A generative model for inorganic materials design.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials A generative model for inorganic materials design

Reference 25

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This paper cites P.et al.Python materials genomics (pymatgen): A robust, open- source python library for materials analysis.Computational Materials Science 68, 314–319 (2013).

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials P.et al.Python materials genomics (pymatgen): A robust, open- source python library for materials analysis.Computational Materials Science 68, 314–319 (2013)

Reference 26

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This paper cites & Liu, Y.Space group constrained crystal generation.The Twelfth International Conference on Learning Representations (2024).

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & Liu, Y.Space group constrained crystal generation.The Twelfth International Conference on Learning Representations (2024)

Reference 27

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Observation c055678b-48ef-4177-af03-5718a3907e7c · outbound

This paper cites Wyckoff transformer: Generation of symmetric crystals.Forty- second International Conference on Machine Learning(2025).

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Wyckoff transformer: Generation of symmetric crystals.Forty- second International Conference on Machine Learning(2025)

Reference 28

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This paper cites Crystalite: A Lightweight Transformer for Efficient Crystal Modeling.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Crystalite: A Lightweight Transformer for Efficient Crystal Modeling

Reference 29

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This paper cites Guiding generative models to uncover diverse and novel crystals via reinforcement learning.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Guiding generative models to uncover diverse and novel crystals via reinforcement learning

Reference 30

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Substitution-Based Analysis of Structural Novelty for Generative Models of Materials APL Materials , author =

Reference 31

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This paper cites Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Clémentine Fourrier, Nikita Kazeev, Chaitanya K.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Clémentine Fourrier, Nikita Kazeev, Chaitanya K

Reference 32

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This paper cites URL https://www.sciencedirect.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials URL https://www.sciencedirect

Reference 33

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Observation b2a6e6d2-3334-477e-be85-049995f03fc2 · outbound

This paper cites Closed-Form Diffusion Models.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Closed-Form Diffusion Models

Reference 34

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This paper cites & No, A.Understanding and mitigating memorization in gen- erative models via sharpness of probability landscapes.Forty-second International Conference on Machine Learning(2025).

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials & No, A.Understanding and mitigating memorization in gen- erative models via sharpness of probability landscapes.Forty-second International Conference on Machine Learning(2025)

Reference 35

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Observation 2877f476-63f1-41bf-8a89-9a886d00c711 · outbound

This paper cites URL https://openreview.net/forum?id=O33LAUliUF.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials URL https://openreview.net/forum?id=O33LAUliUF

Reference 36

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This paper cites Substitution-Based Analysis of Structural Novelty for Generative Materials Models.

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials Substitution-Based Analysis of Structural Novelty for Generative Materials Models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation cites this paper.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Substitution-Based Analysis of Structural Novelty for Generative Models of Materials

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