Paper Citation Record · LEDGER

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data

As of 18 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2506.01860.

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

pith.paper-citation-record.v1
2506.01860 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:37:09.107642Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:13:41.029658Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:37:19.186680Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 313edd8d-30c9-4334-91e1-2edb49b0b437 · outbound

This paper cites an unresolved cited work.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Unresolved cited work

Reference 1

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Observation bcc072d8-dd8e-4bc5-87e4-7d99fb713942 · outbound

This paper cites First principles phonon calculations in materials science.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data First principles phonon calculations in materials science

Reference 2

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Observation ab338805-a934-4e3a-b065-7237c87427fb · outbound

This paper cites Inelastic Scattering , revision 1.0 ed.; California Institute of Technology, 2020.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Inelastic Scattering , revision 1.0 ed.; California Institute of Technology, 2020

Reference 3

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Observation 64ea1e5b-3d14-4f8a-8787-4a18529e1a50 · outbound

This paper cites utt, K. T.; Tkatchenko, A.; M\.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data utt, K. T.; Tkatchenko, A.; M\

Reference 4

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Observation 09115957-ef85-4ab6-afbf-1390cfc4efdf · outbound

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 5

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Observation fcc9aff7-b442-4c84-8a57-a2b556007c7d · outbound

This paper cites A.; Ceder, G.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data A.; Ceder, G

Reference 6

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Observation e7e93e13-20d8-436b-967a-b4bef58ee052 · outbound

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Universal Machine Learning Interatomic Potentials are Ready for Phonons

Reference 7

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Observation dd548f7e-eda2-4ad7-9d78-6b76849e3b73 · outbound

This paper cites atztogo/phonondb.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data atztogo/phonondb

Reference 8

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Observation 7b7cd6bb-bfa7-47b8-9fcb-0ffd9a60fa4a · outbound

This paper cites T.; Li, M.; Cheng, Y.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data T.; Li, M.; Cheng, Y

Reference 9

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Observation 164e0410-1372-438a-90b3-a98d3c2274f0 · outbound

This paper cites P.; Hautier, G.; Chen, W.; Richards, W.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data P.; Hautier, G.; Chen, W.; Richards, W

Reference 10

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Observation 42457e86-c8cd-4bd0-8a74-55ade8a304e2 · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 11

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This paper cites Orb-v3: atomistic simulation at scale.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Orb-v3: atomistic simulation at scale

Reference 12

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Observation 8c8a9315-bb8f-44bb-8e84-6157eb573927 · outbound

This paper cites Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations

Reference 13

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Observation 7fd5a0d8-0eec-488d-8148-78e9a65f041e · outbound

This paper cites Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.537023Z

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Observation 166f79ed-75f3-43eb-a320-fa776d8780a1 · outbound

This paper cites Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing

Reference 15

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Observation 9fcbefe6-ca09-4c78-b093-7177b0d1b97f · outbound

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 16

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This paper cites A foundation model for atomistic materials chemistry.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data A foundation model for atomistic materials chemistry

Reference 17

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Observation d2bd2f14-cdfc-4f9c-8fb4-2767273001d6 · outbound

This paper cites P.; Simm, G.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data P.; Simm, G

Reference 18

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This paper cites P.; Musaelian, A.; Simm, G.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data P.; Musaelian, A.; Simm, G

Reference 19

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Observation e3f013bf-577a-4763-9142-4e33108e594f · outbound

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 20

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Observation 2d0a1dd7-d39b-4e51-93cf-106b666f3c19 · outbound

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Orb: A Fast, Scalable Neural Network Potential

Reference 21

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Observation d2a3a870-3686-4d87-885c-f6a7626789e3 · outbound

This paper cites J.; Ceder, G.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data J.; Ceder, G

Reference 22

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Observation 919d1d2e-a510-45b8-89c5-e7b463fd92be · outbound

This paper cites an unresolved cited work.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:37:09.488283Z

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Observation fd9c1210-a0d3-4dcd-a5f5-38b63b78d100 · outbound

This paper cites Computer-assisted IR spectra prediction -- linked similarity searches for structures and spectra.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Computer-assisted IR spectra prediction -- linked similarity searches for structures and spectra

Reference 24

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Observation 8c4329c2-84fe-492e-b105-e5064006b670 · outbound

This paper cites A deep learning model for predicting selected organic molecular spectra.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data A deep learning model for predicting selected organic molecular spectra

Reference 25

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Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Unresolved cited work

Reference 26

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Observation 27dcc23c-4f0d-44c9-ba2d-a531d2fe1b38 · outbound

This paper cites E.; Kolesnikov, A.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data E.; Kolesnikov, A

Reference 27

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Observation e46ec227-127a-4174-a733-2f79bb833bc4 · outbound

This paper cites Q.; Daemen, L.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Q.; Daemen, L

Reference 28

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Observation 42450d52-8d98-452e-bf8b-d352ff964708 · outbound

This paper cites M.; Stone, M.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data M.; Stone, M

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.427814Z

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Observation fce99977-c201-4b0e-a7f5-495143185654 · outbound

This paper cites N.; Kim, D.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data N.; Kim, D

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.417384Z

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Observation dbce33c6-da5b-4e8a-abc7-8308dc9495f7 · outbound

This paper cites D.; Wang, X.; Abernathy, D.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data D.; Wang, X.; Abernathy, D

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.406930Z

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Observation d1e5acaa-83fb-4b7c-a015-5b0580693de6 · outbound

This paper cites A.; Daemen, L.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data A.; Daemen, L

Reference 32

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Observation 6709f60f-14e3-440b-be01-b7b52dab7bc1 · outbound

This paper cites MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 33

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Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data I.; Bernholc, J.; Lu, W.; Ramirez-Cuesta, A

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.385825Z

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Observation 046c9e0f-26d2-4327-879e-22d4207e7951 · outbound

This paper cites E.; Fern\'andez-Catal\'a, J.; Cheng, Y.; Daemen, L.; Ramirez-Cuesta, A.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data E.; Fern\'andez-Catal\'a, J.; Cheng, Y.; Daemen, L.; Ramirez-Cuesta, A

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.374798Z

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Observation 9f8653cc-ae22-4d37-835b-0054987498fa · outbound

This paper cites L.; Kolesnikov, A.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data L.; Kolesnikov, A

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.362513Z

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Observation 7852727d-ce66-401f-be48-e4587a38832f · outbound

This paper cites Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set

Reference 37

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Observation 5468e855-224e-4ccc-8778-5ca853d80e9d · outbound

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Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Unresolved cited work

Reference 38

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Observation 430b9a24-6cb0-464c-a095-808055516e52 · outbound

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Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data P.; Burke, K.; Ernzerhof, M

Reference 39

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Observation abbbbebb-0dda-4226-a5f6-46602953e182 · outbound

This paper cites Implementation strategies in phonopy and phono3py.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Implementation strategies in phonopy and phono3py

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:09.098033Z

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Observation 56884d75-a4c1-4abc-80ce-f3e437737c7a · outbound

This paper cites First-principles Phonon Calculations with Phonopy and Phono3py.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data First-principles Phonon Calculations with Phonopy and Phono3py

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.327242Z

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Observation 649bc8d5-d21f-46ed-9ce5-f1fbf94f405d · outbound

This paper cites H.; Mortensen, J.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data H.; Mortensen, J

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:37:09.314603Z

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Observation 106bd9c2-276c-463a-9501-f849ba87932c · outbound

This paper cites hݳre׻v Æ [ҘH4zx׾^SS F :4P2h:sFm65m6^x @ P롌J<hӦMS6@.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data hݳre׻v Æ [ҘH4zx׾^SS F :4P2h:sFm65m6^x @ P롌J<hӦMS6@

Reference 43

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Pith citing papers

Observation 5734ed8f-bf81-4fc0-928a-3f49c07bed99 · inbound

Facet: highly efficient E(3)-equivariant networks for interatomic potentials cites this paper.

Facet: highly efficient E(3)-equivariant networks for interatomic potentials Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data

Reference 24

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Observation f3f31ae6-3948-4bbc-9161-6f8f9ac30909 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data

Reference 170

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Source-reported events for the cited work

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