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Paper Citation Record · LEDGER

Accelerating point defect photo-emission calculations with machine learning interatomic potentials

As of 23 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2505.01403.

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

pith.paper-citation-record.v1
2505.01403 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:22:22.256049Z

measured 41 of 41 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T22:41:46.289356Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:41:52.891880Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 90c3ec1e-86db-4901-94a2-427213c215b6 · outbound

This paper cites biomag2d.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials biomag2d

Reference 1

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Observation c2798352-4bd4-4bfa-a50d-487c5bc91520 · outbound

This paper cites Wolfowicz, F.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Wolfowicz, F

Reference 2

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Observation f2fb031a-a39f-4092-bbb3-c2dfff60e3c9 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 3

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

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Observation f4b9d198-8247-4f20-a887-9d64c904a3aa · outbound

This paper cites Sarkar, L.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Sarkar, L

Reference 4

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Observation f99d0e2d-a4a7-4b33-90c2-867068fea771 · outbound

This paper cites Jhuria, V.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Jhuria, V

Reference 5

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Observation 9b2a3933-d021-4649-b0cc-13e44ddddff4 · outbound

This paper cites Jelezko and J.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Jelezko and J

Reference 6

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Observation d30fdf5c-9ef9-4847-9cf7-47b89abfb723 · outbound

This paper cites The nitrogen-vacancy colour centre in diamond.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials The nitrogen-vacancy colour centre in diamond

Reference 7

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Observation ddc3bec8-fe64-4297-8cea-474037235d12 · outbound

This paper cites Jones and P.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Jones and P

Reference 8

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Observation 2bc48846-7c49-49ac-8eab-e35325a4e6a5 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 9

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Observation 74f334d9-f347-413a-90fa-d9d466be9c9e · outbound

This paper cites Gali, Recent advances in the ab initio theory of solid- state defect qubits, Nanophotonics12, 359–397 (2023).

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Gali, Recent advances in the ab initio theory of solid- state defect qubits, Nanophotonics12, 359–397 (2023)

Reference 10

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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.

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Observation 9303e236-e619-4a8b-af5f-65d825138493 · outbound

This paper cites Behler, Perspective: Machine learning potentials for atomistic simulations, J.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Behler, Perspective: Machine learning potentials for atomistic simulations, J

Reference 12

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Observation 94b8cd9a-7eae-4077-ba68-e3326e604fed · outbound

This paper cites Schmidt, M.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Schmidt, M

Reference 13

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Observation 2f1ad563-c404-47d9-9633-3c53c18c747b · outbound

This paper cites Graser, S.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Graser, S

Reference 14

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Source-reported events for the cited work

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Observation bf6ed5cc-148a-4d38-b5cc-1d138dd64eab · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 15

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Observation 975e7d58-6fbf-434b-9051-46874e2ee1d6 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 16

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Observation 7ad6a2fc-c5e1-4510-ba16-0193c7f31290 · outbound

This paper cites Chen and S.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Chen and S

Reference 17

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Observation 4dde7da0-c6e5-43c4-aa0f-188efd26dbc8 · outbound

This paper cites Batatia, D.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Batatia, D

Reference 18

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

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Observation dab6b67e-dfba-406a-8f6a-16f69afc57ea · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 19

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Observation 8848ae81-6a70-4cf4-863c-82d5719116b4 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 20

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Observation a0f5cad9-2a77-42cc-8455-6b55421f9bf0 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 21

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Observation cf43f753-3018-4e6e-9700-145a821b8c09 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 22

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Observation d5f1eef2-07a4-43e4-bba9-6ad1331a10ed · outbound

This paper cites Choudhary and B.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Choudhary and B

Reference 23

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Observation 5b73c214-ac12-4e5d-9c16-9c1a7be76657 · outbound

This paper cites Chmiela, V.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Chmiela, V

Reference 24

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

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Observation 32b8aa44-0265-4dc9-8d3b-adae1ffbc21d · outbound

This paper cites Stocker, J.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Stocker, J

Reference 25

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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.

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Observation aecd310f-8118-43a1-8c85-52b249fdcb74 · outbound

This paper cites Universal Machine Learning Interatomic Potentials are Ready for Phonons.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Universal Machine Learning Interatomic Potentials are Ready for Phonons

Reference 26

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Observation dec783cf-4590-4df2-bda8-9a461f7f4c0a · outbound

This paper cites Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 27

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Observation 936ed5d3-0fb1-465c-8e4e-04235e7233cb · outbound

This paper cites Batzner, A.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Batzner, A

Reference 28

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Observation 3d615b00-48fd-4a40-8e20-b7af7619e394 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 29

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Observation 92e885f2-3d8f-4cf9-979e-56e7b99cae46 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 30

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Observation d699d42c-19ab-4ee6-93d8-52cb6f74a363 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 31

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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.

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Observation a5ad7e9a-40d0-468f-aa16-4a1ed98978d8 · outbound

This paper cites Alkauskas, B.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Alkauskas, B

Reference 32

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Observation ddc5d52b-032c-46dd-9517-a07f5ec2d5e9 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 33

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unresolved
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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.

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Observation dfc51ec0-b6fb-458b-834c-e9992709508f · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 34

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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.

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Observation 777fc60d-d278-4e96-9821-ae25178cdd36 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 35

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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.

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Observation bc43df4f-677d-45fc-b285-1ddc428d5479 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 36

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Observation 657735f3-03e9-4ee1-82e0-9bbab90a73c4 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 37

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Observation bd0bb1e7-66dd-4e17-9b9c-7928c67746ad · outbound

This paper cites Bitzek, P.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Bitzek, P

Reference 38

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Observation 0b4f1b04-a730-4298-8b99-0315a4b42fc5 · outbound

This paper cites an unresolved cited work.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Unresolved cited work

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c40702be-c499-40ce-861d-b62f96fb521a · outbound

This paper cites Togo, First-principles phonon calculations with Phonopy and Phono3py, J.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Togo, First-principles phonon calculations with Phonopy and Phono3py, J

Reference 40

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Pith citing papers

Observation 7937193d-63b1-4356-a72b-3c9489df2a0d · inbound

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects cites this paper.

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects Accelerating point defect photo-emission calculations with machine learning interatomic potentials

Reference 28

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arxiv_id, observed 2026-05-18T22:41:52.895996Z

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Observation 94f4ee8a-f909-4b01-bffe-f384591767a4 · inbound

AI-Driven Expansion and Application of the Alexandria Database cites this paper.

AI-Driven Expansion and Application of the Alexandria Database Accelerating point defect photo-emission calculations with machine learning interatomic potentials

Reference 26

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arxiv_id, observed 2026-05-16T23:18:39.764410Z

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