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Paper Citation Record · LEDGER

Predicting neutron experiments from first principles: A workflow powered by machine learning

As of 18 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2504.19352.

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

pith.paper-citation-record.v1
2504.19352 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:58:55.829125Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:12:09.648375Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T05:12:09.810860Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4aff25a8-39f5-427b-83de-c5b5a02b072f · outbound

This paper cites Shahzad, A.

Predicting neutron experiments from first principles: A workflow powered by machine learning Shahzad, A

Reference 1

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Observation c3ae8bd8-eee5-4ee8-b73c-361360048354 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 2

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Observation 411b0f94-6601-4977-bd8e-baad0a8052eb · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 3

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Source-reported events for the cited work

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Observation d0eaaedc-c5e9-4be0-b0eb-32813ecc094b · outbound

This paper cites Fujii, J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Fujii, J

Reference 4

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Source-reported events for the cited work

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Observation ef9fafd6-7dd8-4012-ba32-d1391d052276 · outbound

This paper cites Hautier, C.

Predicting neutron experiments from first principles: A workflow powered by machine learning Hautier, C

Reference 5

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Source-reported events for the cited work

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Observation 7789318a-632c-47c3-8cb4-608de0eee346 · outbound

This paper cites Huang, F.

Predicting neutron experiments from first principles: A workflow powered by machine learning Huang, F

Reference 6

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Source-reported events for the cited work

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Observation 98428882-aace-40b0-b7ea-c1187f7acd2a · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 5f497777-f9f5-469f-bb4b-e4990081c2f0 · outbound

This paper cites Lookman, P.

Predicting neutron experiments from first principles: A workflow powered by machine learning Lookman, P

Reference 8

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Source-reported events for the cited work

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Observation 374f02f1-aa43-445f-803c-b60fcf0f408c · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 9

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Observation a00662cc-7dc6-4aa3-a594-843b5fdd93b0 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 10

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Observation 983037c2-70b7-48ac-9e70-363558f2a152 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 11

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Observation 862cb9f8-57d6-4610-bef4-976072049c2e · outbound

This paper cites Behler, Four Generations of High-Dimensional Neu- ral Network Potentials, Chemical Reviews 121, 10037 (2021).

Predicting neutron experiments from first principles: A workflow powered by machine learning Behler, Four Generations of High-Dimensional Neu- ral Network Potentials, Chemical Reviews 121, 10037 (2021)

Reference 12

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Source-reported events for the cited work

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Observation f400643a-dea7-4a1a-a86c-af377e90a9b2 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 575b5d88-eba7-470d-81c9-525f13991bf1 · outbound

This paper cites Ehlers, M.

Predicting neutron experiments from first principles: A workflow powered by machine learning Ehlers, M

Reference 14

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Source-reported events for the cited work

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Observation 1af31f3c-4822-4c11-ba90-2f35923d5b4f · outbound

This paper cites Borgschulte, J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Borgschulte, J

Reference 15

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Source-reported events for the cited work

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Observation e5e0ad77-09f2-4319-b952-481c98986444 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 16

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Observation d1ccb8e9-13a2-40c0-8ab1-b2d9b05ee44a · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation cb2d2e36-99e0-4a0d-b935-931effcd461e · outbound

This paper cites Fransson, M.

Predicting neutron experiments from first principles: A workflow powered by machine learning Fransson, M

Reference 18

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Observation 9d6b8cb8-57af-4203-bec2-bfd660e4c2fa · outbound

This paper cites Dynasor 2: From Simulation to Experiment Through Correlation Functions.

Predicting neutron experiments from first principles: A workflow powered by machine learning Dynasor 2: From Simulation to Experiment Through Correlation Functions

Reference 19

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Observation 00703857-bf3a-4556-a819-a8b7f3f0cf12 · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 20

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Observation 7af45877-e38b-44a1-8f9e-08653282a778 · outbound

This paper cites Fan, Improving the accuracy of the neuroevolution machine learning potential for multi-component systems, Journal of Physics: Condensed Matter 34, 125902 (2022).

Predicting neutron experiments from first principles: A workflow powered by machine learning Fan, Improving the accuracy of the neuroevolution machine learning potential for multi-component systems, Journal of Physics: Condensed Matter 34, 125902 (2022)

Reference 21

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Observation 4deb7d2d-df78-4503-ac69-aabe4a1b3190 · outbound

This paper cites Fransson, J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Fransson, J

Reference 22

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Observation f49cf4cc-f983-4492-ac76-2e8ad35cac26 · outbound

This paper cites Lindgren, M.

Predicting neutron experiments from first principles: A workflow powered by machine learning Lindgren, M

Reference 23

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Source-reported events for the cited work

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Observation cdcf98fd-340b-41e3-b0db-c552137e472b · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 24

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Observation d97c75a9-35d8-45de-8f90-3c7d26ed6c8c · outbound

This paper cites Eriksson, E.

Predicting neutron experiments from first principles: A workflow powered by machine learning Eriksson, E

Reference 25

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Source-reported events for the cited work

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Observation 9fa38f9b-72df-49ef-8cd1-98a1e9facffc · outbound

This paper cites Parrinello and A.

Predicting neutron experiments from first principles: A workflow powered by machine learning Parrinello and A

Reference 26

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Observation fa47bff3-82f6-4a30-8197-636812930f1a · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 27

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Observation 1ed65b2c-1682-4e1d-9045-1a8ef220bac8 · outbound

This paper cites Rossi, M.

Predicting neutron experiments from first principles: A workflow powered by machine learning Rossi, M

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8def8ae5-4aa2-4543-a377-e5935c74d9a5 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 271a535e-0879-4340-9943-61c8ca63f46a · outbound

This paper cites Turanyi, A.

Predicting neutron experiments from first principles: A workflow powered by machine learning Turanyi, A

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 48c15a9e-58e6-4815-9e49-376027cabf13 · outbound

This paper cites Dymkowski, S.

Predicting neutron experiments from first principles: A workflow powered by machine learning Dymkowski, S

Reference 31

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Source-reported events for the cited work

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Observation 1f40c0a4-74ac-42c8-9b07-f0a18f3f62b2 · outbound

This paper cites Arnold, J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Arnold, J

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e24034f6-2dec-4fd6-835b-88ed3ef026ff · outbound

This paper cites Rosander, E.

Predicting neutron experiments from first principles: A workflow powered by machine learning Rosander, E

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3e1ef045-9573-443f-88b7-66bf58168290 · outbound

This paper cites Cardona and G.

Predicting neutron experiments from first principles: A workflow powered by machine learning Cardona and G

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 72a68753-5e78-4718-9ea1-1b0e7a447d48 · outbound

This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bb8ff0f9-0415-4a72-b936-5d628feecfbe · outbound

This paper cites Kresse and D.

Predicting neutron experiments from first principles: A workflow powered by machine learning Kresse and D

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 82a1cb1a-4f66-46d4-9445-8921f8f3877a · outbound

This paper cites Kresse and J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Kresse and J

Reference 37

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Source-reported events for the cited work

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Observation ae207892-2627-4115-bdf1-e47ab97051b6 · outbound

This paper cites Kresse and J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Kresse and J

Reference 38

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Observation 89f08ad2-4af0-48d1-8669-b365336f0cd4 · outbound

This paper cites Kresse and J.

Predicting neutron experiments from first principles: A workflow powered by machine learning Kresse and J

Reference 39

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This paper cites Berland and P.

Predicting neutron experiments from first principles: A workflow powered by machine learning Berland and P

Reference 40

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 41

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 42

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 43

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 44

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 45

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This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 46

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This paper cites Plots were generated using matplotlib [48], with color maps from perfect-cmaps [49].

Predicting neutron experiments from first principles: A workflow powered by machine learning Plots were generated using matplotlib [48], with color maps from perfect-cmaps [49]

Reference 47

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Observation 5d91489f-cc04-4ef6-876b-d7d483d0236e · outbound

This paper cites Virtanen, R.

Predicting neutron experiments from first principles: A workflow powered by machine learning Virtanen, R

Reference 48

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 49

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This paper cites Ulmestrand, Perfect-cmaps (2025), (accessed 2025-04- 10).

Predicting neutron experiments from first principles: A workflow powered by machine learning Ulmestrand, Perfect-cmaps (2025), (accessed 2025-04- 10)

Reference 50

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 51

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 52

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 53

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This paper cites Hellman and I.

Predicting neutron experiments from first principles: A workflow powered by machine learning Hellman and I

Reference 54

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Observation c75cb8ff-fb1e-4e72-babb-fe1b3c8f0122 · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Tadano, Y

Reference 55

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Observation 1cd5c423-8a29-47d5-b650-6c62a160e460 · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Monacelli, R

Reference 56

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Observation b2b218d3-f144-4766-963f-981239effcb2 · outbound

This paper cites Perrichon, N.

Predicting neutron experiments from first principles: A workflow powered by machine learning Perrichon, N

Reference 57

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Observation 9736a11f-fef1-4000-b722-96e012c46912 · outbound

This paper cites Chen and S.

Predicting neutron experiments from first principles: A workflow powered by machine learning Chen and S

Reference 58

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This paper cites an unresolved cited work.

Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 59

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Observation 85e012dc-818a-47c0-bf6e-4a1257894de4 · outbound

This paper cites Merchant, S.

Predicting neutron experiments from first principles: A workflow powered by machine learning Merchant, S

Reference 60

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Observation 4b945936-7a29-4f56-8faf-a490f22fb376 · outbound

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Predicting neutron experiments from first principles: A workflow powered by machine learning Unresolved cited work

Reference 61

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Observation a3f84f2b-03a6-42dc-851c-f3b29af8ca7f · outbound

This paper cites A foundation model for atomistic materials chemistry.

Predicting neutron experiments from first principles: A workflow powered by machine learning A foundation model for atomistic materials chemistry

Reference 62

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Pith citing papers

Observation 7bf48207-b84a-4a84-a2b9-a437d2a58184 · inbound

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements cites this paper.

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements Predicting neutron experiments from first principles: A workflow powered by machine learning

Reference 49

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