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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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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 16

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Observation 29373873-4a2d-44b2-83f1-eeaededb2522 · outbound

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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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 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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Observation dafbae1c-3d42-48d7-b9eb-711f0bafe6fe · outbound

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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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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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=sha256:12b770477a34dc983a9b6edb0fa6c011badba00ecca507994b6805c936b67cfd

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:f56fbb4685dd133931cfa5baf042b81535df956820cd5121f48b75144b199bb4

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:bc0ad620d6e3de06ec2e9030de42d871547a08c2e66cd578456496cc89b87569

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:dbe8a7a0c7987d17d3c6b07ac2bbe70d505b25dbc9e8e2921da2e74a6f785835

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:c28fdbff7e5f1483043342567b89c93a621aa69a696473fade8b8721a4fbe1a2

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:bbe90f9ef0d4e2df7ee32330eeab95d9c13d11bc5fbfbe2799f8271452edc9a8

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=sha256:1c6c9cfd14ed96463a60245023174ac49e3dc798a0f53121b493b0e28437ee06

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=sha256:28b561982749f41e9524c086648c2fe9c2f6dd204478b601e69e1a3ab52b127d

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=sha256:0f605a69e09ca3882d18ee2ae248cfc2392e2417b585b99d7100c40a1f623177

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=sha256:3871d90da349c00cd9026c213d54ae2e711c7db33ec9ab2fc5833962bb843c44

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=sha256:7a5f64b173440723576d29fa1639b978411c57f79c29c6568a027cb3e2240f36

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:d9c7ea81e254ee202a1f7f3b07bc39290a71273a3a70d9a8bdfd3e2c97241af5

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:fa796ff5abb6792bb02b9d2d2ab36ee4299c918c22e48920893515340dc28723

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=sha256:096fb6895c7f5ee0e5791cc342799ca2081b3106fed2aa5f8cf502bf0716cf05

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=sha256:6b5ae5e36826382fce682ea716a9ee0ea3076ff1d2fdd3ee097d8088e8df4bf0

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:be4463c60363e11ceefd66ae914c8a83b692149ba8ed50027c65157bae3407cf

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:f166155f58ae2accf36d82097dcb79527e45eff7b72851639e7d0bda129d7056

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:da3c18a80cb5674c8b8a25c752d21ef013d3d9e097c8c1f6f31bf048bbb1caf4

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:ef5e23fb502e85d17ce2f76084856f64a0e252144e950c2464ac0d9deeee57db

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=sha256:4277cea34d6dfb7e6a474b53d77f22f7736a760c2ebe604b9ad2e63e697b3e94

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=sha256:599b726f17da402b18b8ae7e4b0c1fcee9b43d761ba8a0402f7899304616f029

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:edd87eff31c62b83eba0150a1fb005a9455089cfc2b27661361721eda2d972d9

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=sha256:5ca9e31fb0fe4da1493f7e942886e2a70cd1e8855be0b096564f2faaeb15f340

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:be65fa64a4609b3bb984aa32568f812d8f6ff74181bd395803ac2584c9022f57

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=sha256:0ac6c411ee329abcc71ccc8263b67600685f2a1531ee93af11b63e38d5573903

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=sha256:2e25dc5f557e89039ba1bf2a083c0191a4cd8c0806a96cae638fc19f2545aef6

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:a01ae0a7f3b47ee4b6ef2c1fb397ae56e6bbbb06f9538ad23ce69ad816181607

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=sha256:9f67e4e713101918482561f189867a1c2d42242e6aae9ade4b5aa932e7dbd85b

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=sha256:2a613190d48d4a6b223d05717394b4fefcad2b524708c4eae59af37af4b82539

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=sha256:9ee469d71c2782d8da79a07e835da42bf24098c77ecb4a0d39f04f9927fc0899

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=sha256:66573cd07b5460ff9992887f6ace811ca4c53bac30ffc9aab63144fe0b7851c1

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=sha256:7e38fd2274e1b98454341d589d0f41ef067b2a5c77881681cc0b70d5a58fa239

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=sha256:74a357f62db566e423572c47e3c7b4b38fdef967513c0001d3ddd0b1ffbcc99a

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=sha256:577998199b4901b53d91f8c2e374c1678f844ca7f84970e116935eeedb8f025c

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=sha256:23c33727d19cecd524be19d6d2031f270e593a567c328a867caf9f2aecb1b683

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=sha256:68bdf11f997dffb6c1f725119d2ac41939ba2d81485baf581d8fba7f25e423fe

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=sha256:0916ae0db8824cd3e4e926cfea3e9fb7dee70367b24d141c881032fee81dea42

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:d17bc04d32b3759404118b4cc96a613e8e6ea44cf696fe12a43147e3c0970a6d

Pith citing papers

No inbound Pith citation observations are available.