Paper Citation Record · LEDGER

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.16398.

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

pith.paper-citation-record.v1
2501.16398 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:00:43.581751Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 2b49a107-c80a-4692-a783-c6b84d0c62d0 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.387142Z

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Observation 46a15f0f-2452-4e3e-858f-78ffe2fa7a07 · outbound

This paper cites Taking materials dynamics to new extremes using machine learning interatomic potentials.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Taking materials dynamics to new extremes using machine learning interatomic potentials

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:44.370033Z

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Observation 1376e0b8-b32f-4870-a9dc-a4f000aff3c7 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.353985Z

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Observation 92911c7b-d00a-4883-873b-1cb40785d80b · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.333190Z

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Observation 85a97410-9bb7-4c23-82de-ea8db8c8de9d · outbound

This paper cites Perspective on density functional theory.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Perspective on density functional theory

Reference 5

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Observation 3a325417-75f0-4a3b-a430-c6ebb143c3ab · outbound

This paper cites & Von Lilienfeld, O.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Von Lilienfeld, O

Reference 6

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Observation c69226fc-ce5f-45d4-b738-2cd2aee89430 · outbound

This paper cites & Kim, C.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Kim, C

Reference 7

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Observation 57abf23e-fae4-4346-b2ad-0eb604b15bf4 · outbound

This paper cites & Tkatchenko, A.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Tkatchenko, A

Reference 8

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Observation 78f9d7af-9dbb-4f8e-b161-1d3bc9aeb844 · outbound

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.257258Z

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Observation 64294b8a-ac6d-4af3-9921-7bfe544beec5 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.235745Z

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Observation 36afa086-aa2e-45a3-ba7f-cad975e54448 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.215282Z

Source-reported events for the cited work

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Observation 13f4ff4c-777e-4109-bc5e-cf598babd28d · outbound

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

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Perspective: Machine learning potentials for atomistic simulations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:44.198022Z

Source-reported events for the cited work

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Observation 37d08a5b-b0cb-4d7f-96d3-a8cd12e3ac0a · outbound

This paper cites P., Kermode, J., Bernstein, N.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments P., Kermode, J., Bernstein, N

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:44.174950Z

Source-reported events for the cited work

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Observation 365d867a-a59c-41bc-b1bb-a4cdc885963e · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.148969Z

Source-reported events for the cited work

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Observation b9f83636-040b-4ea4-b858-1330e1e780f9 · outbound

This paper cites & Parrinello, M.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Parrinello, M

Reference 16

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Source-reported events for the cited work

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Observation ffc26780-a6fe-4e73-8dd8-ab1c9a526b46 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 46244f42-1f49-441b-a98b-29e2ed6d1edc · outbound

This paper cites Deep Potential: a general representation of a many-body potential energy surface.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Deep Potential: a general representation of a many-body potential energy surface

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:00:43.632750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b14a78d3-5538-4348-a64e-e41b9c3b73b4 · outbound

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

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments P., Simm, G., Ortner, C

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:44.089558Z

Source-reported events for the cited work

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Observation d8c96178-acfb-4590-b867-72d24cb768ea · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:44.068914Z

Source-reported events for the cited work

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Observation 3aef331f-5cb1-4083-8421-68104e08ff3e · outbound

This paper cites & Vasilakos, A.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Vasilakos, A

Reference 21

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Observation 4a6f62e1-4f72-4193-9d62-f1dd169da37a · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 22

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Observation d47899f7-f3ac-4f5b-a12d-a29cb8ec3f8b · outbound

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 959a983a-dbc2-44e3-ad96-dfd3da66f222 · outbound

This paper cites The development and comparison of molecular dynamics simulation and Monte Carlo simulation.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments The development and comparison of molecular dynamics simulation and Monte Carlo simulation

Reference 24

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Observation bb93faa7-dd7c-49a0-b5b7-a21e05137a75 · outbound

This paper cites & Beratan, D.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Beratan, D

Reference 25

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 26

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This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 27

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Observation 6a021472-14e1-43f5-949a-393763a7e675 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 28

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Observation c316721e-35f1-451c-9d52-380643f10e62 · outbound

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 29

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Observation a494fc79-2fa1-4e51-b981-92c1f595ed93 · outbound

This paper cites & Hinton, G.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Hinton, G

Reference 30

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Observation e0e78671-0577-4b1c-9466-1b12e41ca620 · outbound

This paper cites & Johnson, I.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Johnson, I

Reference 31

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Source-reported events for the cited work

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Observation e6ba35b7-3e53-4917-a6e8-3a592fc84a39 · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 32

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 578d78db-2e8d-48a0-bdfe-72155aa468a4 · outbound

This paper cites Atom -centered symmetry functions for constructing high -dimensional neural network potentials.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Atom -centered symmetry functions for constructing high -dimensional neural network potentials

Reference 33

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Observation bc47f6b2-682b-4d4e-a796-5f094499fa1b · outbound

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 34

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Observation e43639a6-f144-42e8-bd92-31ad84fa4f80 · outbound

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Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:43.769564Z

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Observation 91c2d844-aef0-4e7c-a1a7-d52f797aa863 · outbound

This paper cites & Ogata, S.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Ogata, S

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8a128e93-3571-414a-be96-64513ed3f6b0 · outbound

This paper cites L., Gasparotto, P., Csá nyi, G.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments L., Gasparotto, P., Csá nyi, G

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:43.736004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f6bd573d-652e-49fb-96fb-13b3d102a19b · outbound

This paper cites an unresolved cited work.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:00:43.720501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 189950e9-5d5f-40bb-8ee8-e4a8d5aa2f28 · outbound

This paper cites S., Fu, N., Dong, R., Hu, M.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments S., Fu, N., Dong, R., Hu, M

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:43.693210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4b94f529-fbba-417c-968a-60494d7cd8be · outbound

This paper cites & Parrinello, M.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments & Parrinello, M

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e2048e11-7533-4410-a1d1-1b9d1e275dd1 · outbound

This paper cites Atom -centered symmetry functions for constructing high -dimensional neural network potentials.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments Atom -centered symmetry functions for constructing high -dimensional neural network potentials

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:43.672421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ca4defd-348e-44fa-902a-86bc7ec87750 · outbound

This paper cites S., Nebgen, B., Lubbers, N., Isayev, O.

Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments S., Nebgen, B., Lubbers, N., Isayev, O

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.