Paper Citation Record · LEDGER

Universal Machine Learning Potential for Systems with Reduced Dimensionality

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2508.15614.

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

pith.paper-citation-record.v1
2508.15614 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:53:52.438756Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T23:16:47.814591Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T23:18:39.746142Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy13
unresolved30
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66b7cb03-3adf-4345-9655-ddf0b4150722 · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Behler, Perspective: Machine learning potentials for atomistic simulations, J

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:48.764516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e78bd6ed-9773-4628-8c25-ccf3178a7fb0 · outbound

This paper cites Schmidt, M.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Schmidt, M

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:48.802348Z

Source-reported events for the cited work

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Observation 79f8ef41-99c6-4173-83cf-3cbeab8d9467 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:57.049369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ef00696-69c7-4c1c-91f2-d9b0aa9ed876 · outbound

This paper cites Chen and S.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Chen and S

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:56.866795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e48c084-6e17-4526-a1fd-b39e95a7e384 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:56.726040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f70d0aa5-de02-48c4-af20-3c2899c613bf · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Universal Machine Learning Potential for Systems with Reduced Dimensionality MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:48.986707Z

Source-reported events for the cited work

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Observation 803aebde-2f08-4b16-bb33-98e63b32aa6d · outbound

This paper cites CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling.

Universal Machine Learning Potential for Systems with Reduced Dimensionality CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.091156Z

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Observation 9fa31991-fa1f-48a2-baa7-aac7f7b66832 · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.184378Z

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Observation a392fc98-72b1-47f1-aaa7-6ddbbdd5235a · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Orb: A Fast, Scalable Neural Network Potential

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.242163Z

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Observation 87c408ee-f960-4372-afc0-1895bf7c399e · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.319554Z

Source-reported events for the cited work

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Observation 515b014b-7ce0-461f-b671-9900bee866d6 · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.382700Z

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Observation 2ee1bf62-2de8-489d-8da2-24e01b45a290 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:56.515691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64d0fc30-5d64-4de4-b5a9-39749a77d03b · outbound

This paper cites Focassio, L.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Focassio, L

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:56.368241Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ebc3d623-b068-4a52-8a5b-6bba9c92efec · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:56.171654Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69bb6ba4-1c8c-4814-8ff3-819c3277eb13 · outbound

This paper cites Jain, S.P.Ong, G.Hautier, W.Chen, W.D.Richards, S.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Jain, S.P.Ong, G.Hautier, W.Chen, W.D.Richards, S

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:55.968420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9a98e0d-9d4b-43e7-86b9-812c47717c2e · outbound

This paper cites Schmidt, T.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Schmidt, T

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.760994Z

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Observation a7be53ea-0697-4652-92e4-e6f8da7d4f80 · outbound

This paper cites Devereux, J.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Devereux, J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:55.841703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a104612f-dc54-4d64-9998-582647e28cd1 · outbound

This paper cites Eastman, P.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Eastman, P

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:55.634552Z

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Observation f24f586c-8cfe-4562-b2eb-5945e7461dca · outbound

This paper cites Eastman, B.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Eastman, B

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:55.369143Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5883f97b-dc66-4137-b154-c23f081614f1 · outbound

This paper cites Ganscha, O.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Ganscha, O

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:55.086703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 197a2399-14f3-4750-b39c-11edccf47f53 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:54.946180Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e7908aa-1899-4c49-a6df-1a91fe666f21 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:54.795975Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb46d5b7-2c7b-4f4e-b58c-407d0a91e433 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:50.308707Z

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Observation 3cf4cc21-3f83-48da-b0ba-6fbc2a2a5534 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:54.615873Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b418e0e-459a-4214-8b19-0d2a92efeb29 · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:50.500112Z

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Observation 862179eb-aa2e-4c26-9239-a9fc5f8398f0 · outbound

This paper cites Bochkarev, Y.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Bochkarev, Y

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:50.560948Z

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Observation 5eb923a4-d260-4fb1-b8be-ca264b3dc3e0 · outbound

This paper cites A foundation model for atomistic materials chemistry.

Universal Machine Learning Potential for Systems with Reduced Dimensionality A foundation model for atomistic materials chemistry

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:50.650452Z

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Observation 3885b6a8-0061-4b7d-b805-8ca2f51ccce6 · outbound

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

Universal Machine Learning Potential for Systems with Reduced Dimensionality Orb-v3: atomistic simulation at scale

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:50.714619Z

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Observation fc6d6271-7bc0-4f4f-b848-be1ea71ab4dc · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:54.372281Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 457d54f6-b811-4078-8017-8dd5c3613126 · outbound

This paper cites Sanchez-Gonzalez, J.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Sanchez-Gonzalez, J

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:54.177652Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f712cf3b-ae99-41ba-b940-064ade82952b · outbound

This paper cites Batzner, A.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Batzner, A

Reference 31

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Observation c68a4160-b33f-4458-9a84-7406fe88fbf4 · outbound

This paper cites Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Phys.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Phys

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.094822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6169b0a-4a85-4a91-9840-7a0b553d3120 · outbound

This paper cites Zhang, H.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Zhang, H

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:53.901008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0bd9352-46b2-4e3b-b7be-b8d045329853 · outbound

This paper cites Zhang, X.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Zhang, X

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:53.576832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37644f9b-d88d-4a34-aa17-a9409d6bb852 · outbound

This paper cites an unresolved cited work.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:53:53.422311Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d704610f-ce0b-415f-a84b-262237f6607d · outbound

This paper cites MatterGen: a generative model for inorganic materials design.

Universal Machine Learning Potential for Systems with Reduced Dimensionality MatterGen: a generative model for inorganic materials design

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.371733Z

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Unavailable: canonical work link unavailable.

Observation 9901183b-fdb6-4e77-9b73-2cc7be4490a9 · outbound

This paper cites Riebesell, H.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Riebesell, H

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.450979Z

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Unavailable: canonical work link unavailable.

Observation 10737738-015d-4cc1-a3ec-cdf719fb98c7 · outbound

This paper cites Merchant, S.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Merchant, S

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.575869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ff53236-67d9-43ba-94e8-cafee7c2ff6c · outbound

This paper cites A generative material transformer using Wyckoff representation.

Universal Machine Learning Potential for Systems with Reduced Dimensionality A generative material transformer using Wyckoff representation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.776224Z

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Observation 5c89d860-aa66-457a-a4e9-fbcceabbecc8 · outbound

This paper cites Fredericks, K.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Fredericks, K

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:51.937238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3131b1ed-9a55-4093-807c-099873f71ab7 · outbound

This paper cites Hjorth Larsen, J.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Hjorth Larsen, J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:53.267481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2db7add-f473-4a56-b9bf-97eb7087e111 · outbound

This paper cites Bitzek, P.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Bitzek, P

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:53.039512Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5533938f-9027-4c64-b318-ebe391288268 · outbound

This paper cites Kabsch, A solution for the best rotation to relate two sets of vectors, Foundations of Crystallography32, 922 (1976).

Universal Machine Learning Potential for Systems with Reduced Dimensionality Kabsch, A solution for the best rotation to relate two sets of vectors, Foundations of Crystallography32, 922 (1976)

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:53:52.829043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 88a4a2f2-dac0-40db-8a71-99bcf0feb1ca · inbound

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

AI-Driven Expansion and Application of the Alexandria Database Universal Machine Learning Potential for Systems with Reduced Dimensionality

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:18:39.748007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.