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Paper Citation Record · LEDGER

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

As of 22 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 3 inbound Pith citation observations for arXiv:2502.02748.

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

pith.paper-citation-record.v1
2502.02748 v4

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:21:13.654582Z

measured 20 of 20 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:52.435092Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

17 of 17 outbound references displayed

  • verified exact3
  • verified fuzzy5
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 4ddb6c61-5278-415a-80d2-b88694916f44 · outbound

This paper cites It remains consistent under periodic permutations of the indices (Economou, 2010).

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction It remains consistent under periodic permutations of the indices (Economou, 2010)

Reference 3

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raw_fallback, observed 2026-08-09T11:21:13.961219Z

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.

source=pdf_text observed=2026-08-09T11:21:13.625444Z digest=sha256:0d2d3b203f9aa9c311ee1212d075b82e48d8f1320d29ab03ea06c55ae3a46aa5

Observation 5db982b0-5f11-434c-821e-02fa2eefead7 · outbound

This paper cites MatterGen: a generative model for inorganic materials design.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction MatterGen: a generative model for inorganic materials design

Reference 10

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no resolver link, observed 2026-08-09T11:21:13.620657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.620657Z digest=sha256:0e2a4327fd18ad4281168852588c175d3682772fbcc75d8bac76c3d63fc28e07

Observation 0c0c0fc2-8eb2-482b-a3d8-317bd493a33d · outbound

This paper cites The initial global node feature h0 global is obtained by a linear transformation on h0 local and follows an activation function.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction The initial global node feature h0 global is obtained by a linear transformation on h0 local and follows an activation function

Reference 12

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raw_fallback, observed 2026-08-09T11:21:13.945654Z

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.

source=pdf_text observed=2026-08-09T11:21:13.630323Z digest=sha256:bb833fa75f545dc315ab95d4ba36e6e7b7df09257b4586de4f50a51ff53dc1fa

Observation 51324aa6-e1b9-4d5c-bc30-42d10ab1eed6 · outbound

This paper cites The batch size is standardized at 64, and the models are trained using the L1 loss function.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction The batch size is standardized at 64, and the models are trained using the L1 loss function

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-09T11:21:13.928492Z

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.

source=pdf_text observed=2026-08-09T11:21:13.635086Z digest=sha256:6c53f81d460ee847791f49233bb8591c1e0f02a6e837c7321d58163f0935d41e

Observation fa47f6e1-1307-48dc-b7ab-e49e29e8f608 · outbound

This paper cites The batch size is standardized at 64, and the models are trained using L1 loss function.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction The batch size is standardized at 64, and the models are trained using L1 loss function

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-09T11:21:13.913125Z

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.

source=pdf_text observed=2026-08-09T11:21:13.639909Z digest=sha256:e293f7d94e148e60bdc2e737a91a4d18d50654ac3cc64a0e3ed53abb75c19728

Observation f365ee26-bdaa-4237-aea6-5294e9712dd2 · outbound

This paper cites However, their reciprocal space component is non-trainable and presented only as an optional variant in Appendix J.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction However, their reciprocal space component is non-trainable and presented only as an optional variant in Appendix J

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-09T11:21:13.896729Z

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.

source=pdf_text observed=2026-08-09T11:21:13.644395Z digest=sha256:f6e2b5efa334a3ab93a4156834405c09163f3e834252278d6a595fa94cd7f318

Observation 70bdfbf3-ebe0-432f-b0b3-6046c3fbb7d7 · outbound

This paper cites an unresolved cited work.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Unresolved cited work

Reference 17

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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.

source=pdf_text observed=2026-08-09T11:21:13.654582Z digest=sha256:fbd1f6a45532104d4746d9973bd6d5c2f97798ba5dd3aec133baeb7aeb2af2a2

Observation 07bc5733-ad40-462d-9ac9-813099f21a48 · outbound

This paper cites Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 1978

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no resolver link, observed 2026-08-09T11:21:13.575638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.575638Z digest=sha256:90513363d479aa6a495d7754de4486848d8aad3c040f4d7fb9dde38f5952aab3

Observation 16b91426-8d68-45e9-a7ab-49dab07a08f5 · outbound

This paper cites Directional Message Passing for Molecular Graphs.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Directional Message Passing for Molecular Graphs

Reference 2004

Resolution
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no resolver link, observed 2026-08-09T11:21:13.581227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.581227Z digest=sha256:578a992d449e21aee4f9b64e07a4458f0d2f2d20e7e4d4a14f20fd5cae7e3dd3

Observation 7ba4d20e-ac91-44e4-aae8-9d4a5c120203 · outbound

This paper cites CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling

Reference 2014

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verified exact
local_arxiv, observed 2026-08-09T11:21:13.818982Z

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.

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Observation baded040-99ca-4f65-9c5b-514a6075ba9f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Adam: A Method for Stochastic Optimization

Reference 2015

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.591214Z digest=sha256:f39f5eabf06366ca6ebb6335116ab68ffbe6d07e57ff5377ec67ac23d83ecf97

Observation 6fa58bdb-979f-4d19-b531-f4efb3cad430 · outbound

This paper cites an unresolved cited work.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Unresolved cited work

Reference 2017

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raw_fallback, observed 2026-08-09T11:21:13.881214Z

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.

source=pdf_text observed=2026-08-09T11:21:13.649831Z digest=sha256:c37fa51ac92cca12398ee7bd171f6648f4d8d8700086dd3cb782c3751c3df6a8

Observation c84ce131-697a-43e8-8dbb-3251fbefe8a4 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 2018

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unresolved
no resolver link, observed 2026-08-09T11:21:13.601417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.601417Z digest=sha256:625d3a111b2e06fb7c9b25fc7cd8a65893e00cf93897170f553f0e15df521a3e

Observation 59325647-878d-41ed-af7d-f6d3a4e96310 · outbound

This paper cites Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding

Reference 2019

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verified exact
local_arxiv, observed 2026-08-09T11:21:13.744855Z

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.

source=pdf_text observed=2026-08-09T11:21:13.606151Z digest=sha256:070a2debea6c156361f706e98019976a94c4a19616832a1bcd23f7223f2a7f8b

Observation 5156bf44-a9f4-4d91-8654-492cf25d7d29 · outbound

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

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Reference 2022

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no resolver link, observed 2026-08-09T11:21:13.611449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.611449Z digest=sha256:59ada3b025903e03af27f2e371313cbe99777a5e7329d53f20dc3d7d66bfcc79

Observation be733ef4-c5a4-48e7-9803-22310f315e4b · outbound

This paper cites Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Reference 2023

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verified exact
local_arxiv, observed 2026-08-09T11:21:13.783388Z

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.

source=pdf_text observed=2026-08-09T11:21:13.596393Z digest=sha256:cf200a2b586586859fbc6bc6242d17f30c898c9dbbe90ac71b0ad73a8ebe9dd9

Observation 9f5f8e05-ebbf-4d44-9a7c-7bcdf8b4d69a · outbound

This paper cites Capturing long-range interaction with reciprocal space neural network.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Capturing long-range interaction with reciprocal space neural network

Reference 2024

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no resolver link, observed 2026-08-09T11:21:13.615891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:21:13.615891Z digest=sha256:5af1c6520c4b28fd2fae1b93479e93f40df74c276b8a943aca65ed5ae15f38e0

Pith citing papers

Observation f106c278-45c3-421d-b0f9-6ba816947929 · inbound

Universal crystal material property prediction via multi-view geometric fusion in graph transformers cites this paper.

Universal crystal material property prediction via multi-view geometric fusion in graph transformers ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 38

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no resolver link, observed 2026-08-06T15:39:52.435092Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:39:52.435092Z digest=sha256:2757268f873f732888bdd7cf821d22d725e3a2ac2ae6e99aff78bbceb8a1c2bf

Observation 39f9046f-7051-4dea-9b6d-c5fa079d31a1 · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 286

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local_arxiv, observed 2026-07-11T16:18:08.062622Z

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.

source=arxiv_source observed=2026-07-11T16:17:26.963678Z digest=sha256:09f2715db99c1f640fd2c0060c74b1757e3c108ecb4cf07517f55f2dd6884ad3

Observation 27e4db33-803f-4d82-86e5-7ed633a803c1 · 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 ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:23:13.643808Z digest=sha256:1d97b369c9095412c1175304b9ac150729232f35eac1ac85328bc6e46474ac82