Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:23:17.541677Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 100 of 137 outbound references and 7 inbound Pith citation observations for arXiv:2506.21935.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:23:17.541677Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T05:45:47.307353Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
100 of 137 outbound references displayed
External citation measurements
2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications XXX . model
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Behler and M
Reference 5
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Batzner, A
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Phys
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Batatia, D
Reference 10
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Sch¨ utt, P.-J
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Bochkarev, Y
Reference 12
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Cheng, Cartesian atomic cluster expansion for ma- chine learning interatomic potentials, npj Comput
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Musaelian, S
Reference 14
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Observation 6cc27da3-1cb4-4985-865f-b9f31930efc6 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Musil, A
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Jacobs, D
Reference 23
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Merchant, S
Reference 24
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Poltavsky and A
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications A foundation model for atomistic materials chemistry
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Observation add9e8d0-aed1-4fe0-8c98-e75afdecd51f · outbound
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Awais, M
Reference 28
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Zhang, X
Reference 29
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Choudhary, B
Reference 30
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Chen and S
Reference 31
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Chanussot, A
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Focassio, L
Reference 40
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Dusson, M
Reference 41
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Reference 44
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Liang, P
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Liang, Z
Reference 57
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Zhang, B
Reference 59
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Maron, H
Reference 60
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Torabi, C
Reference 61
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Vignac, A
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 65
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Zhang, G
Reference 69
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Reference 71
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Moellmann and S
Reference 72
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Radova, W
Reference 74
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Observation babfb38a-0d01-4e6a-927a-40639e8e30f5 · outbound
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Fan, NEP dataset repository (2025), accessed: April 2, 2025
Reference 76
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Novelli, L
Reference 77
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Kulichenko, B
Reference 78
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Reference 80
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Advances in modeling complex materials: The rise of neuroevolution potentials
Reference 81
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Reference 82
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications van der Oord, M
Reference 83
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Reference 85
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Reference 87
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Observation 416c5e1a-0ce5-4d58-bd34-53a4ecd5bccb · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Wen and E
Reference 88
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Observation 75754dbf-dc99-4dde-a822-1c8a5fc8b625 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Kellner and M
Reference 89
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Observation f22fc7d9-4c86-4888-b16d-60779470ccc2 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Edeling, M
Reference 90
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Observation 2fd293eb-cdd5-4f36-82b4-cca115aaecc1 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Venturi, R
Reference 91
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Observation 24e8db9a-42d1-4a68-a0ae-328697eee258 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Jiang, T
Reference 92
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Observation 11e5223b-6b3d-4c0e-ae00-cd37a98a7c3f · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
Reference 93
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Observation 40edaa4a-adf1-4fff-bc38-04f3bde08021 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Zhang, D.-Y
Reference 94
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Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
Reference 95
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Observation 9b9d5d8c-fb3e-443e-b1a8-b4b2ffefd7d0 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
Reference 96
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Observation 79d2ea2b-dcc0-4b9f-bf3b-b63a53fc4dec · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Unresolved cited work
Reference 97
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Observation 0730ce3c-c80a-4e35-bcbf-56741eebf824 · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Uncertainty in the era of machine learning for atomistic modeling
Reference 98
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Observation d2b546f5-54f6-4345-86cf-bebba7efbaba · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Reference 99
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Observation c7b35d96-c8c6-466a-a600-67ba75835c3d · outbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Zhang, Modern monte carlo methods for efficient un- certainty quantification and propagation: A survey, Wi- ley Interdiscip
Reference 100
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Observation 51bdde56-6d37-4519-80ff-e459ed3f1221 · inbound
Fine-tuning of universal machine-learning interatomic potentials for high-entropy alloys with application to 2D (Mo,Ta,Nb,W,V)S$_2$ Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 43
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Observation 82c1b4c5-ab0a-4fc2-bf97-35a3cbb58ad7 · inbound
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 22
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Observation b9c686de-1254-495d-9222-626e84a00fba · inbound
Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 73
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Observation 1b603fb1-6064-4719-a766-47e756e2df4a · inbound
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 166
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Observation 8ef28d6b-0393-45a4-93da-e1621e83f8d3 · inbound
Fine-Tuning a Universal Machine-Learned Interatomic Potential for Oxygen Plasma Interactions with WS$_2$ Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 42
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Observation 9e6bffdb-8cb1-4f51-8020-fe88b22a0e86 · inbound
Fine-Tuning a Universal Machine-Learned Interatomic Potential for Oxygen Plasma Interactions with WS$_2$ Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 42
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Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Reference 39
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