Paper Citation Record · LEDGER

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.18141.

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

pith.paper-citation-record.v1
2505.18141 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:38:22.989687Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T16:17:26.963678Z

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

38 of 38 outbound references displayed

verified exact1
verified fuzzy16
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

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

Outbound references

Observation 86315309-39c6-4c95-9599-fa011dee40fd · outbound

This paper cites D., Batra, R., Chapman, J.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics D., Batra, R., Chapman, J

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:38:19.708152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80672c2b-73f3-43b0-8db5-c890e2aace5a · outbound

This paper cites Harder, better, faster, stronger: large-scale QM and QM/MM for predictive modeling in enzymes and proteins.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Harder, better, faster, stronger: large-scale QM and QM/MM for predictive modeling in enzymes and proteins

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:38:23.134349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 13703ede-6720-4927-91f5-de1af0b10aa1 · outbound

This paper cites & Clementi, C.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Clementi, C

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:38:19.817212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 655976ee-9f47-4033-8143-be6207ccdf46 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:27.238043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d549e80-8241-4ff6-805c-d3e6d4dfbc90 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:27.117840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ab690c63-4027-48ce-a6b6-60f88765aae9 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:27.023314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c3a4505-3fa4-495b-95de-f2d22487b2ef · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:26.942365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 67dc2009-9a3c-4cb6-bbc9-a46f15a3aaf4 · outbound

This paper cites G., Moffat, R.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics G., Moffat, R

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.863179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9f4b50dc-a74e-4abb-ab03-8cd9b83f20bc · outbound

This paper cites J., Rowley, C.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics J., Rowley, C

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.740279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2afbb2b3-89eb-40b7-9525-b053156ad0fc · outbound

This paper cites & Ong, S.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Ong, S

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.639217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 86cdfe5e-7120-46cd-b4c9-9f6a35ff98f5 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9da282c-1ce5-40df-9647-0a820f2e6554 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:26.552634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a8d58377-b316-49f0-8616-9e3532ba4722 · outbound

This paper cites & Ong, S.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Ong, S

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.433082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f91ebcdc-a943-44ff-946e-342e6e47d5a7 · outbound

This paper cites Benchmarking and advancing neural network potentials for molecules and materials.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Benchmarking and advancing neural network potentials for molecules and materials

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.271137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5c15222f-73bc-437a-a881-010a43fcf434 · outbound

This paper cites P., Simm, G., Ortner, C.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics P., Simm, G., Ortner, C

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:26.089076Z

Source-reported events for the cited work

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Observation 91f7f191-9f6c-4062-a80d-273e38eb4173 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics KAN: Kolmogorov-Arnold Networks

Reference 16

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Source-reported events for the cited work

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Observation 080fff10-d856-480f-a6ea-5c08d2182624 · outbound

This paper cites & Kuriyan, J.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Kuriyan, J

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:38:21.142785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77bf4641-8d58-48aa-b709-ec36193aad7f · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:25.950930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 923423cd-5269-4602-b532-3207fb7dc5f5 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:25.783595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b2775ddc-ac07-402e-b9a8-b8038c143216 · outbound

This paper cites Convolution hierarchical deep-learning neural networks (c-hidenn): finite elements, isogeometric analysis, tensor decomposition, and beyond.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Convolution hierarchical deep-learning neural networks (c-hidenn): finite elements, isogeometric analysis, tensor decomposition, and beyond

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:25.643737Z

Source-reported events for the cited work

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Observation 1d4ab24e-d905-48e0-8502-0ca04126922e · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:25.504131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99caf09a-3559-43cf-890e-528c6a0f2584 · outbound

This paper cites explanatory.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics explanatory

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:25.404838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3c23c302-0285-43bf-9cff-c95756c853b1 · outbound

This paper cites Interpolating neural network: A novel unification of machine learning and interpolation theory.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Interpolating neural network: A novel unification of machine learning and interpolation theory

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:38:21.680755Z

Source-reported events for the cited work

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Observation 4195d538-59eb-44df-9ba9-a7f26be956a9 · outbound

This paper cites A., Behler, J., Dellago, C.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics A., Behler, J., Dellago, C

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:25.245292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9634c2e-7b24-457a-af3a-0fe060eb904e · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:25.146696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ebbd8c3d-d26d-441b-b238-60285d813e77 · outbound

This paper cites & Markland, T.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Markland, T

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:25.025867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ae02b8c-fc74-400f-b74a-de9346014699 · outbound

This paper cites & Jiang, B.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Jiang, B

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:24.896068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1b8122eb-d677-4e4c-912b-bfd8cd94522d · outbound

This paper cites & Weinan, E.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Weinan, E

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:24.752021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9587fc6a-5572-415f-9cfd-e5d36d3ac500 · outbound

This paper cites & Jiang, B.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics & Jiang, B

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:24.621036Z

Source-reported events for the cited work

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Observation fd4e27be-a4f4-47f0-b518-08563220a9af · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:24.501532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e033f35c-7fea-4bd1-94ef-75190e2eac08 · outbound

This paper cites Cartesian atomic cluster expansion for machine learning interatomic potentials.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Cartesian atomic cluster expansion for machine learning interatomic potentials

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:24.323948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bb4f8507-dd59-403e-be67-5d0c73ad9332 · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:24.186456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation de46f79c-1d9a-4915-bd1f-18a1cfeae27e · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:24.060807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0dd0dae7-08d9-4018-9b45-4c5bb35bb2bb · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:23.928254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1222880c-c92d-4cdb-a470-df0ae335f0e5 · outbound

This paper cites K., Jun, S.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics K., Jun, S

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:23.792170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 878b30e6-ea16-49c9-8d63-08dbc58162bf · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:23.667272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e2268c9-dded-41b3-be21-1c525c3d5ec8 · outbound

This paper cites Kronecker delta: N {c} s = ∪kN (k) s W (k) j (x(i)) = δij.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Kronecker delta: N {c} s = ∪kN (k) s W (k) j (x(i)) = δij

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:38:23.476514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e4d0d9e-89ff-4fcb-be35-a4a098ee705d · outbound

This paper cites an unresolved cited work.

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:38:23.292692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 1bf9b53e-9779-4dcd-8997-1700df9e4c50 · 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 INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics

Reference 284

Resolution: metadata mismatch; local_arxiv, observed 2026-07-11T16:18:07.865187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.