A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) | Paper Citation Record

Observation 62eeaf34-3644-458e-be46-2a0f3b4c9571 · outbound

This paper cites Behler, Perspective: Machine learning potentials for atomistic simulations, The Journal of chemical physics 145 (17) (2016).

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Behler, Perspective: Machine learning potentials for atomistic simulations, The Journal of chemical physics 145 (17) (2016)

Reference 1

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Observation 29feea6f-b066-4bb0-9778-b50e11b0e36d · outbound

This paper cites Vamathevan, D.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Vamathevan, D

Reference 2

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This paper cites Raccuglia, K.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Raccuglia, K

Reference 3

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 4

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 5

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 6

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Observation 6d0e3324-6faa-40c1-bc0f-bdb124314577 · outbound

This paper cites Altona, D.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Altona, D

Reference 7

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 8

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Observation aac34453-5cca-4dcd-8123-d3bae0a2c047 · outbound

This paper cites Behler, M.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Behler, M

Reference 9

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 10

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 11

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Observation e5922872-d99c-4c17-8d1f-7a9387fef300 · outbound

This paper cites Batatia, D.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Batatia, D

Reference 12

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 13

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Observation b0407854-de02-402f-bb34-f555ad02f979 · outbound

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

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Phys

Reference 14

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Observation 0d341818-8790-4fd1-8049-bffbc96e7e2b · outbound

This paper cites Dusson, M.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Dusson, M

Reference 15

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Observation 838d1c70-aa81-483d-9c56-98e42f304711 · outbound

This paper cites Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products

Reference 16

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 17

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Observation 55d150a7-f0eb-4ebf-af5f-504e25c791df · outbound

This paper cites Gilmer, S.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Gilmer, S

Reference 18

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Observation c6001213-4191-4418-a5df-c6d39447923e · outbound

This paper cites Gilmer, S.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Gilmer, S

Reference 19

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 20

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Observation b6bc2544-c456-40bf-963a-b008fa31a211 · outbound

This paper cites Kasneci, K.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Kasneci, K

Reference 21

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Observation 77c213ed-bb1c-4161-b014-094ba23b952a · outbound

This paper cites Chanussot, A.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Chanussot, A

Reference 22

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 23

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 24

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Observation 914ae326-4e34-4c9a-9a10-81fa5849a9db · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) On the Opportunities and Risks of Foundation Models

Reference 25

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This paper cites A foundation model for atomistic materials chemistry.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) A foundation model for atomistic materials chemistry

Reference 26

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 27

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Observation f8895e77-40c2-41f7-aba5-09fe9e98477a · outbound

This paper cites Merchant, S.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Merchant, S

Reference 28

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This paper cites DPA-2: a large atomic model as a multi-task learner.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) DPA-2: a large atomic model as a multi-task learner

Reference 29

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Observation ba0ef160-ac7e-4c36-ac31-604d545d82d2 · outbound

This paper cites Choudhary, B.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Choudhary, B

Reference 30

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 31

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Observation 5ffabf3c-040e-471a-8696-4ecbd34b4960 · outbound

This paper cites An Extendable Cloud-Native Alloy Property Explorer.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) An Extendable Cloud-Native Alloy Property Explorer

Reference 32

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Observation dcad0946-5a01-4090-b398-d857c6e5bc98 · outbound

This paper cites Focassio, L.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Focassio, L

Reference 33

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Observation 09b4f72b-25a8-4413-940a-fb9010770482 · outbound

This paper cites Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 34

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 35

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Observation 16deb100-c1df-453b-90c2-9ba75bf2ffe9 · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 36

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Observation ca61edac-c7b7-458d-a5b1-21bf8063500a · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 37

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Observation e035f326-d1af-4e49-98cd-db376f283cb9 · outbound

This paper cites Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Reference 38

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This paper cites Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

Reference 39

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 40

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Observation 7a2820ca-5a3a-41e6-ad07-f451f6f35bcd · outbound

This paper cites Transferability of datasets between Machine-Learning Interaction Potentials.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Transferability of datasets between Machine-Learning Interaction Potentials

Reference 41

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This paper cites Casillas-Trujillo, A.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Casillas-Trujillo, A

Reference 42

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Observation 1245b1c2-5eb4-47e7-a605-ebb8e6490fba · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 43

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Observation 3c08848b-d1e3-4204-a72e-96e19dfb8ed7 · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 44

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Observation d4794948-0530-4072-9d2c-fa6db711491d · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 45

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Observation f49526b2-c22a-4e3d-bb81-d3babef77989 · outbound

This paper cites Liang, P.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Liang, P

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:17.034642Z

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Observation 6db9afe4-a7af-4236-82f3-08f399d20a4b · outbound

This paper cites Liang, Z.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Liang, Z

Reference 47

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Observation ad42f79b-223b-42f0-8b7a-15507a9730e2 · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 48

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Observation a483063d-e8e0-4d1b-a948-c612710e4ea5 · outbound

This paper cites Atomic Cluster Expansion without Self-Interaction.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Atomic Cluster Expansion without Self-Interaction

Reference 49

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Observation f46f0639-31da-47dd-b35c-0c3ca29c62a9 · outbound

This paper cites Zhang, B.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Zhang, B

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:17.004335Z

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Observation f4eb2e25-2278-4742-bd45-569bcb89d66c · outbound

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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.990289Z

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Observation dc185929-56d1-4e6a-a76f-16793210672e · outbound

This paper cites Surrogate models for vibrational entropy based on a spatial decomposition.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Surrogate models for vibrational entropy based on a spatial decomposition

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:40:16.179740Z

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Observation b0a98431-a1af-433d-8fcc-034a1fbfb36d · outbound

This paper cites Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion

Reference 53

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Observation 151e7d4d-5d31-4603-97a8-889d2b6ff420 · outbound

This paper cites Vignac, A.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Vignac, A

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.975037Z

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Observation 6fa3cbb1-bed6-42e6-b5ad-a688e16b7039 · outbound

This paper cites Invariant and Equivariant Graph Networks.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Invariant and Equivariant Graph Networks

Reference 55

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Observation 6e2dc26a-926b-4d83-8b86-df46b6723068 · outbound

This paper cites Tajbakhsh, J.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Tajbakhsh, J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.960824Z

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

Observation 8a23461e-8b94-4eec-90e1-9109c54a351f · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.946047Z

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 5659d62f-49f9-479e-997f-ce27ffc7260e · outbound

This paper cites Zhang, G.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Zhang, G

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.931142Z

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 6e420fd2-83db-43b8-85a6-40b4574767b2 · outbound

This paper cites Devlin, M.-W.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Devlin, M.-W

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.916989Z

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 030f6860-fde7-4b8e-bc9b-93d2af86aaff · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.902081Z

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 57dfb110-e0cb-40d3-b249-35a7b412fc5a · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.886773Z

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 68f03bb9-35ec-43f6-80cb-4360e33c1295 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.840493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53fe1e11-5b25-481e-abdb-0fc7f8488ba6 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.871813Z

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 178e2412-6e2e-4871-a281-2297465c7aa3 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.856984Z

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 07944b47-177f-4f36-a700-361a9b59fbec · outbound

This paper cites Henkelman, B.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Henkelman, B

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.843212Z

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 c3e8e4dc-5cb2-4981-8af8-9f113891e77a · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.828420Z

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 c971a830-674b-4f17-9a80-7407c701a420 · outbound

This paper cites Hyperparameter Optimization for Atomic Cluster Expansion Potentials.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Hyperparameter Optimization for Atomic Cluster Expansion Potentials

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:40:16.092149Z

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 f3cb6f65-3b8f-4b55-a38e-7c31ed2ce528 · outbound

This paper cites Zhang, G.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Zhang, G

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.810888Z

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 494698d1-d2d2-4ee9-a4c0-abdf3f797f1c · outbound

This paper cites Guénolé, W.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Guénolé, W

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.795293Z

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 357bff43-c811-4d82-be92-d3cd35a66cf8 · outbound

This paper cites Grigorev, L.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Grigorev, L

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.780244Z

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 5c7422e3-59f5-4887-8cc2-8b149334c9d6 · outbound

This paper cites Byggmästar, A.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Byggmästar, A

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.765249Z

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 fe3e191f-031b-4321-a385-2cff3c454490 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.750848Z

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 d76c1d54-62c0-48f7-a9e5-5402d98bd5c1 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.887379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 342390d2-ccdc-49e5-877b-297c65ad7a10 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.724593Z

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 08b66437-15c7-4417-8a09-d3a6534fed32 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.895904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ada9a5dc-426a-4db5-9d0b-4cda45769dd1 · outbound

This paper cites Alkhulaifi, F.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Alkhulaifi, F

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.698286Z

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 be1cec90-c857-4858-b649-681a763e7864 · outbound

This paper cites Sivaraman, J.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Sivaraman, J

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.683520Z

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 2601977b-4c68-475f-9d1a-fb0dc63a24f7 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.909228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88395875-ee4a-4abc-a741-8da48a32e8ba · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.667949Z

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 a66523b5-4299-4f51-950a-84aff5b8f88f · outbound

This paper cites Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.919197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb11d518-8cf7-4369-92aa-28e821dec1c6 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.652364Z

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 7fd462d8-460b-4c6b-998a-41000cd2bd51 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.637619Z

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 041f56f4-e8d4-471f-9c8d-59ec1c78bced · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.621983Z

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 d0ab13e7-f376-4583-89d6-d60163722489 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.606216Z

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 e4e2362d-7790-4647-ae54-0b488e44bfba · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.590964Z

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 54c5efd9-8c71-49e3-91a3-f703ee0fe5fc · outbound

This paper cites Siddiqui, N.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Siddiqui, N

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.574546Z

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 98ea4a94-eb80-4887-b527-23c0f4859e92 · outbound

This paper cites Vandenhaute, M.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Vandenhaute, M

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.559909Z

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 c1546395-a240-4d24-9f82-e2b8c54eed4e · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.544657Z

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 1ffe87c4-ce0a-440f-bf3d-d7dcdea429b6 · outbound

This paper cites an unresolved cited work.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:16.529191Z

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 237882f1-fd90-46c1-a5ae-298f319f8315 · outbound

This paper cites Paszke, S.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Paszke, S

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:16.512698Z

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 3792f7e9-7b0e-45f8-ac48-baf302100a0c · outbound

This paper cites e3nn: Euclidean Neural Networks.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) e3nn: Euclidean Neural Networks

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.967677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c965b384-a1de-4127-92e8-bdb62d1828d3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Adam: A Method for Stochastic Optimization

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:15.972373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers