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Paper Citation Record · LEDGER

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires

As of 12 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2608.06662.

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

pith.paper-citation-record.v1
2608.06662 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

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measured 72 of 72 standing notices

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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

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation 1ab22581-a987-435a-bc64-cadc5f3ea07c · outbound

This paper cites This result is consistent with the strong representation of bulk crystalline environments in the pretraining data.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires This result is consistent with the strong representation of bulk crystalline environments in the pretraining data

Reference 1

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Observation 970f0f58-898e-4462-8bd1-a175738135bc · outbound

This paper cites The zero-shot models pro- vide the closest overall agreement with the DFT disper- sion, with phonon eigenvalue RMSEs of 0.785 THz for MACE and 0.540 THz for ORB.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires The zero-shot models pro- vide the closest overall agreement with the DFT disper- sion, with phonon eigenvalue RMSEs of 0.785 THz for MACE and 0.540 THz for ORB

Reference 2

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Observation ad327c81-42d3-4b74-9a23-5ddd7b25bcfc · outbound

This paper cites For MACE, fine-tuning gives the lowest mean relative surface-energy error, ap- proximately 3.08%, compared with 3.33% for training from scratch and 16.14% for zero-shot inference.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires For MACE, fine-tuning gives the lowest mean relative surface-energy error, ap- proximately 3.08%, compared with 3.33% for training from scratch and 16.14% for zero-shot inference

Reference 3

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Observation 9824c6f5-a88c-4b31-876e-445f69207f72 · outbound

This paper cites RMSD is used to identify structural drift relative to the initial configuration after removing rigid translation and rotation.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires RMSD is used to identify structural drift relative to the initial configuration after removing rigid translation and rotation

Reference 4

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This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 5

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Observation da3a786f-7098-42b6-8b15-da997c580178 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 6

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Observation 8da83faf-ad55-4982-be8f-9629e93e84ba · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 7

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Observation 1911f13a-6f2f-463d-9feb-be90a95f62cc · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 8

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Observation ff976d93-e229-4ff8-9e1b-13985eac39da · outbound

This paper cites Mishin, Machine-learning interatomic potentials for materials science, Acta Materialia 214, 116980 (2021).

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Mishin, Machine-learning interatomic potentials for materials science, Acta Materialia 214, 116980 (2021)

Reference 9

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This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 10

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Observation f193275f-e9ce-4a30-b3b2-4eba26520b2d · outbound

This paper cites Batzner, A.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Batzner, A

Reference 11

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Observation e9cab222-bc4e-4ecf-8970-fa1b1bc1d3a7 · outbound

This paper cites Batatia, D.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Batatia, D

Reference 12

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 13

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Observation a2cf5e00-4afa-41ff-a136-a59c78ff4c3e · outbound

This paper cites Chen and S.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Chen and S

Reference 14

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Observation 7678cee0-99ac-410f-bb84-c28d1fb78542 · outbound

This paper cites Musaelian, S.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Musaelian, S

Reference 15

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 16

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This paper cites Creed, T.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Creed, T

Reference 17

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 19

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This paper cites Schmidt, T.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Schmidt, T

Reference 20

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 21

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 22

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This paper cites Benedini, A.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Benedini, A

Reference 23

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 24

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Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 25

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Observation e583d7ca-bc6a-415e-967f-43ab4a8e7827 · outbound

This paper cites Pa¸ sca, H.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Pa¸ sca, H

Reference 26

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This paper cites Verdi, F.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Verdi, F

Reference 27

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This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 28

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Observation 0890349c-2821-4c86-909e-77030290dd99 · outbound

This paper cites Zhang, G.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Zhang, G

Reference 29

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Observation 8729b15c-9732-45a5-9449-e28e1273362f · outbound

This paper cites Focassio, T.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Focassio, T

Reference 30

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This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 31

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Observation 5e06acf5-44b6-4a05-8f2e-d191f063ad0d · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 32

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Observation 912fe7f7-b122-43f0-b078-83d0ba657620 · outbound

This paper cites Rumiantsev, M.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Rumiantsev, M

Reference 33

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Observation 2b4f97b1-1d80-49ad-b239-e1c111e84d76 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 34

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Observation 1c50dcfc-ed04-4ec2-92e7-24557ee885d2 · outbound

This paper cites Kim and B.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Kim and B

Reference 35

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Observation 03e3f915-1554-4ae4-ab1e-feddaa53bf7d · outbound

This paper cites Cheng, Latent ewald summation for machine learning of long-range interactions, npj Computational Materials 11, 10.1038/s41524-025-01577-7 (2025).

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Cheng, Latent ewald summation for machine learning of long-range interactions, npj Computational Materials 11, 10.1038/s41524-025-01577-7 (2025)

Reference 36

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no resolver link, observed 2026-08-10T22:59:30.991380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:30.991380Z digest=sha256:1d24d773429cf9e4973aef2d92e9b01e365224e24728ec7a821637e6b6dbd411

Observation 45094081-ff5e-4eb9-aa37-aaec40a07259 · outbound

This paper cites Kabylda, J.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Kabylda, J

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.887486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:30.996932Z digest=sha256:feae18e66fe5633b4c7fdd9a95f415c997dc077e1c4a6529c757686fb1f28336

Observation 2c60d399-b78a-4535-8a69-546ebbd799d5 · outbound

This paper cites Batatia, W.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Batatia, W

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.871627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.002593Z digest=sha256:d4bf1f6721e4786dc3c6b4ab5adce7f4668ddb84f7326c2b3ce3b227c097b04c

Observation e3233d9d-6683-4623-a3d1-ef394a00b0e7 · outbound

This paper cites Riebesell, R.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Riebesell, R

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.007122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.007122Z digest=sha256:d4983edcfa8ea7e7575228a2e3a62f54addce3d8b10c8b001d98fe4173350410

Observation 7f922c8c-7ddf-477b-b14a-6f90fd8d87a9 · outbound

This paper cites Maxson, A.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Maxson, A

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.847083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.012368Z digest=sha256:84c5b42faf72682d77b34ee08f754a7c2e31ae704c174cb2f68576ab241561c0

Observation 65e104ba-2f57-4c34-9876-6ae83c819f3f · outbound

This paper cites Jacobs, D.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Jacobs, D

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.832885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.017136Z digest=sha256:1557483fbca04daf51f1950c1a881a91a53066623c2a571806f0b4a2a95beb83

Observation 85cff09c-a078-4d63-9b10-79dc823d5b8c · outbound

This paper cites Focassio, L.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Focassio, L

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.818635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.021751Z digest=sha256:969a23b834a5c3e1bbfb8152383e6030ab247bf80f50ffd2a66bb8ca9f34e078

Observation d69df198-b59c-4140-af37-f6954e099b11 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 43

Resolution
verified exact
doi, observed 2026-08-10T22:59:31.303932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.026297Z digest=sha256:adc1d31dd888704d100daf7dc4ddd102a2d40a3928f8af0db46caac455fbe674

Observation a72d6d41-d665-4940-914c-094f47c19105 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.802958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.031391Z digest=sha256:186e6dff8d2aad1ceffe2b0b6bac34cef21c31d9b12af627a83fd255866ee9aa

Observation 11418b62-8e3e-4ecc-9a4e-ba005bc8d447 · outbound

This paper cites Huang, B.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Huang, B

Reference 45

Resolution
malformed identifier
doi_truncated, observed 2026-08-10T22:59:31.289176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.037111Z digest=sha256:23024240dce4f785ddc16bcb6ba5862cf6090e1fa917b56131faaddefa8bf537

Observation d3575652-1c75-46bf-89ab-0452f3e7213e · outbound

This paper cites Alampara, M.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Alampara, M

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.787229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.042515Z digest=sha256:f44fd013f95d78628337b6a6f0ba3394eee6ab7e7d1bfb9b54c96343dfe45deb

Observation a5f2e2a7-f124-4e19-88f0-a6ac68994cd3 · outbound

This paper cites Iftimie, P.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Iftimie, P

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.770811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.047053Z digest=sha256:a669ea09c65cf541467f5e76030a59483c6ecb9eb448c6c8c689c88273ebda9b

Observation 7e2abdc2-a691-4d8f-bab4-9b5cae638c3d · outbound

This paper cites Zanineli, S.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Zanineli, S

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.757207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.051710Z digest=sha256:ae334ff166a04d3d9abfee7f34ca4614d26e881d0e72d1f5b68b8a1bafbe4bc6

Observation 9a0d0fd9-ed7b-43d6-8e41-3838536f9ef0 · outbound

This paper cites Hjorth Larsen, J.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Hjorth Larsen, J

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.743215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.056819Z digest=sha256:63871fb527eb53fd24b926ce8b128f1fdbde4389709835565f2981dc23fda06d

Observation d1deaf0e-48d5-42a3-aa60-2bb5cfb1e398 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 50

Resolution
malformed identifier
doi_truncated, observed 2026-08-10T22:59:31.273630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.062563Z digest=sha256:f218d8d8ef9e96ed2a701b5b0234ff6e8ff5539eb0e74f5a00b5d9e8f5130b41

Observation d97ee20e-6511-4906-babf-29525208bef7 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.711242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.073553Z digest=sha256:b37eedfde78db37a9615382082c641deb4fe45d3c0fefc6bc48eefff8a941916

Observation 4f35c36f-f267-40a8-aea2-dc58460b36ad · outbound

This paper cites Pokluda, M.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Pokluda, M

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.695828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.079158Z digest=sha256:6378144c8849b3d908e63ea8e46eb02e0ccb8ce8d5cb9ea661ac80274a695549

Observation c1fad4ce-bcb3-4c60-b440-879b6d64ee97 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.680819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.083749Z digest=sha256:50c58147e182e45cce0cb0e5fe42443ff40d0ccecdeae19ac7a5ec603450dbe3

Observation 14a5bafb-319c-4375-8c05-5858c5de501d · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.664687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.089189Z digest=sha256:58fd4f3c29c7311d1c48ff829b1a96709d2edb4a8ab32f74dd7319c522aa175f

Observation bd975911-1011-4b60-84a1-5d3621b9dd7f · outbound

This paper cites Zhang, A.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Zhang, A

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.094414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.094414Z digest=sha256:ebed955c8f513847a39ebc83440b3f5f194cabecfd70241f8c45410145b726fc

Observation b547f269-847a-4940-aab0-bd2f30e22cda · outbound

This paper cites Bochkarev, Y.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Bochkarev, Y

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.099114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.099114Z digest=sha256:f86b3e1beb3db2e30fcda788590979012522a58d2dd5fc24916a267f62dd32f9

Observation 008bd161-a00e-4708-b44e-1eee21e624ed · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.648503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.103838Z digest=sha256:7815db61a857d4b5a103f524b0e3499ed79e6d3c0638ab6bc1663001ff48132e

Observation 2708a574-e352-45c3-84cc-7e8f24c88746 · outbound

This paper cites Batatia, P.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Batatia, P

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.109322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.109322Z digest=sha256:280110ec6a660a81864f67fa5ecbbbe19da247b751f2fe10e04f9c55a84fde47

Observation b4752444-1808-4e22-b1af-7b06298359e4 · outbound

This paper cites Rhodes, S.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Rhodes, S

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.611074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.120132Z digest=sha256:5f90ab3a6b5de93247fece56fe37173895a5e8a5fe459a9357649187857305ef

Observation d30e3667-0854-4be9-9802-a8f5f6c3d9d1 · outbound

This paper cites Gubaev, V.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Gubaev, V

Reference 62

Resolution
verified exact
doi, observed 2026-08-10T22:59:31.238644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.125370Z digest=sha256:7b1fe427d3e6f57049da676f9fd583bd87ac1c229b2c029d082b78877b6b2b6b

Observation dd5618f9-601a-4b68-ad37-eb8f69ce6bf4 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.595330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.129863Z digest=sha256:da00825b120f178474241f4a457c0447cfe804eb78e5b6a2f608fcd6f47b26d4

Observation 11ee05b1-ba49-4027-82f9-f9c6e44fe285 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.134663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.134663Z digest=sha256:ebbf797d73fea3c9531d7cfec9e2a02cda0138e1e37b0f1792dc52642e7a8b46

Observation 984c17a6-881e-4936-a702-f9ceca22d0e1 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.579280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.139823Z digest=sha256:3ca37345084512efeb346c0529f20f8e058a812fcbcae35320537bd7dfa3e48f

Observation 2116cb50-d401-4df9-bb2e-f4f81a19acb8 · outbound

This paper cites Kloppenburg, L.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Kloppenburg, L

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.562083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.144411Z digest=sha256:4319d08d947fc88d2a2c3850c81968e8ff15abff60974f6c3c255a74f0ec1de8

Observation fd74790d-f96c-49a9-8138-3ffeda9d3f3c · outbound

This paper cites Cross-Geometry Transferability Assessment of Foundation Machine Learning Interatomic Potentials: From bulk materials to atomic nanowires.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Cross-Geometry Transferability Assessment of Foundation Machine Learning Interatomic Potentials: From bulk materials to atomic nanowires

Reference 67

Resolution
malformed identifier
raw_fallback, observed 2026-08-10T22:59:31.546534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.149043Z digest=sha256:9f5256b31dbfd81cfc90527bb1d688996278780655fee2ee23063fe1bd38850c

Observation 2aa2b39a-5a98-4cec-bd52-068c35649cdb · outbound

This paper cites Batatia, P.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Batatia, P

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.154553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.154553Z digest=sha256:d68a13162acb784adf510b173d599dcee61634b1499ac25ba6a6c2c361663485

Observation 1669cb96-de79-4522-b525-e35ecf3e5c48 · outbound

This paper cites Neumann, J.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Neumann, J

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.159312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.159312Z digest=sha256:b95eb22c2621892630cf91b901659aca35bd913de2d9741717dfb23bf42e5bcf

Observation 44c822dd-e961-47dc-a66f-b2cfe8341d9b · outbound

This paper cites Rhodes, S.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Rhodes, S

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.520129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.163965Z digest=sha256:ad9cffdb3ff2e27e975719d0e73db52beaca59d036176ff8eb4f5dd0432611fc

Observation d5139a70-92f0-4711-8ca2-d09ebe052276 · outbound

This paper cites Gardner, graph-pes: train and use graph-based ml models of potential energy surfaces (2024).

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Gardner, graph-pes: train and use graph-based ml models of potential energy surfaces (2024)

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.727147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.168677Z digest=sha256:660d383bf02ceeccf81ca2b1c6f5d2a33ca42b761e469483cf3050fde7d1dccd

Observation 44fd775e-fbf8-4569-9ecd-3111b70e96f1 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 72

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:59:31.503742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.173282Z digest=sha256:128a704541f50ad0777621e8117a51cfb26d14ea38b73340b53e6918200b8c3c

Observation ab84c892-3a87-4eb2-83f9-9abb3d8232c5 · outbound

This paper cites an unresolved cited work.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Unresolved cited work

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-10T22:59:31.178496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:59:31.178496Z digest=sha256:5fe34af9dd4ff2a3ae708619dbe977b9255b1c0cf20f082bfdb61d2f79f43a4f

Observation 4153c873-c234-437e-bdfd-bd3549a84aae · outbound

This paper cites Hjorth Larsen, J.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Hjorth Larsen, J

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.486962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.183720Z digest=sha256:f51228066d0c401d31e880bb177fceec1825ed08a578d8e36167bdee6be23aa0

Observation 5f0e77ad-b446-4528-a3a2-263a3353b29c · outbound

This paper cites Focassio, T.

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires Focassio, T

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:59:31.471194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T22:59:31.188687Z digest=sha256:5128dd358b2d46534c97cb5da0b431065f25d93c4bf35299f805d557ede9ced8

Pith citing papers

No inbound Pith citation observations are available.