Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:31:09.370859Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2508.03405.
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 2026-08-06T04:31:09.370859Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
50 of 50 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 4640c514-04ff-4e6b-a12c-4851585398be · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Friederich, F
Reference 1
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Observation 26d7b2a9-4a14-4a32-9e87-76393c44bc47 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Ceriotti, Beyond potentials: Integrated machine learning models for materials
Reference 2
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Observation 1dfebf15-d60d-4512-9cad-7dc553d2103e · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Zuo, et al., Performance and Cost Assessment of Machine Learning Interatomic Potentials
Reference 3
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Observation f3b5bc69-4f4d-44e8-be4a-d58d042769b6 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Jacobs, et al., A practical guide to machine learning interatomic potentials--Status and future
Reference 4
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Reference 5
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Observation 51d07068-7db2-4bff-b2df-971b00b7a3ee · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Liang, et al., Atomic cluster expansion for Pt--Rh catalysts: From ab initio to the simulation of nanoclusters in few steps
Reference 6
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Observation 51a511ea-2048-4d3b-8146-305f54b19549 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 7
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Observation 0f6e9d53-58c9-41c6-9fdb-b78ba6419754 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
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Observation fa61d3ba-e569-4f73-ac89-1c1c5782d70c · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 9
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Observation 1e82ed8b-a7a2-45bb-8efa-26208e0f366e · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Podryabinkin, K
Reference 10
Source-reported events for the cited work
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Observation 6f176824-f930-4005-9e94-e48b469911f0 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials
Reference 11
Source-reported events for the cited work
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Observation 01b71c52-b196-4716-a44a-1c022e5d0f13 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Lysogorskiy, et al., Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon
Reference 12
Source-reported events for the cited work
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Observation 079c642a-4fc7-4205-9cfc-1495136238ca · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Bochkarev, et al., Efficient parametrization of the atomic cluster expansion
Reference 13
Source-reported events for the cited work
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Observation e7cc17ca-b0ae-481e-adeb-4b457eb67842 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Xu, et al., GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Reference 14
Source-reported events for the cited work
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Observation 15b59d50-5a37-4b71-9739-f1228555cd78 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 15
Source-reported events for the cited work
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Observation b56b9af5-1379-4639-89b2-7246638d5b86 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 16
Source-reported events for the cited work
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Observation fd94f14b-3301-4e86-a84e-05b8b4c03c84 · outbound
Reference 17
Source-reported events for the cited work
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Observation 1f5bc08e-51a7-43fa-a292-b3ad7114b814 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Klimanova, N
Reference 18
Source-reported events for the cited work
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Observation d05b1135-b91d-4b36-bd49-345cc6c8ab41 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 19
Source-reported events for the cited work
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Observation 9364d76e-2f3e-4f8c-9eef-3d9ccdc41229 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials van der Oord, M
Reference 20
Source-reported events for the cited work
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Observation ee27fd09-fb5b-4016-94ff-161f81329b12 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
Reference 21
Source-reported events for the cited work
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Observation 25a05d9c-9a88-4ba3-9d5e-68050232762b · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 22
Source-reported events for the cited work
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Observation 1fd60f50-d4a5-4a26-86df-de431095a078 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Lysogorskiy, A
Reference 23
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Observation c570bfb8-08ba-4b14-a7da-82e2e5ea3b8f · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Shuang, et al., Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction
Reference 24
Source-reported events for the cited work
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Observation 8fe9bc81-4423-427d-b29f-6c0ec64f60dc · outbound
Reference 25
Source-reported events for the cited work
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Observation cef73eb7-7821-4fa8-b0c3-d32d1c45a739 · outbound
Reference 26
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Observation f009c432-3103-4fb6-918c-ce76ac2f6fda · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Byggm \"a star, K
Reference 27
Source-reported events for the cited work
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Observation f44c0ea6-344a-438e-8803-ff8476053c0a · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Reference 28
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Observation bb208731-f61a-4613-8bf8-2ce81d93960d · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 29
Source-reported events for the cited work
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Observation be197ddf-d9f2-4570-9604-aff13bc7cb6a · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 30
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e594dc57-4651-4331-9bc2-a25b64dbc613 · outbound
Reference 31
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca8f8a20-0be7-478c-ad0f-ada636903ec7 · outbound
Reference 32
Source-reported events for the cited work
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Observation 69731bb6-c212-49de-8e46-30352421ed56 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 33
Source-reported events for the cited work
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Observation d771db5e-feb5-4260-a3b1-958591cdb263 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Schwalbe-Koda, S
Reference 34
Source-reported events for the cited work
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Observation 07839e90-be49-4722-a40d-2f5cc49ee6f8 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 35
Source-reported events for the cited work
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Observation e4a6154d-071b-46b5-8e21-e0af2909bff3 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials A foundation model for atomistic materials chemistry
Reference 36
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Observation 1458d762-3f28-4c11-ab36-5208c8c43d6d · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Batatia, et al., The design space of E (3)-equivariant atom-centred interatomic potentials
Reference 37
Source-reported events for the cited work
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Observation 0c44077a-d316-4f75-abb0-eefd066a6ccf · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Drautz, Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer
Reference 38
Source-reported events for the cited work
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Observation 6f36a05c-33db-42aa-b811-c22acad91741 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Dusson, et al., Atomic cluster expansion: Completeness, efficiency and stability
Reference 39
Source-reported events for the cited work
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Observation 8b9b9f40-735d-4116-87f0-048740e17d7f · outbound
Reference 40
Source-reported events for the cited work
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Observation bb1a82e2-c85c-453d-bd88-c79e8c203b4c · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 41
Source-reported events for the cited work
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Observation 75466e2f-d86f-47ba-83dc-62803e14a29c · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 42
Source-reported events for the cited work
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Observation 4e47291c-ea4e-4b67-83f2-6be35c8ccb6e · outbound
Reference 43
Source-reported events for the cited work
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Observation 47e8c9ff-767b-40b8-8071-edda4a57b7e5 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 44
Source-reported events for the cited work
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Observation 374dd2ba-fc8f-4e8d-8eab-71a6be4a6e60 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 45
Source-reported events for the cited work
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Observation d32cb2c2-c857-409d-9e67-020913388526 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 46
Source-reported events for the cited work
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Observation 868cd594-80e7-4af2-9fad-f0923a77b89b · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work
Reference 47
Source-reported events for the cited work
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Observation c6bee55b-9454-46fd-8aba-39b0c9ef3594 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool
Reference 48
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1b9f0cc6-d101-49c9-9b76-91b5da59139b · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials , " * write output.state after.block = add.period write newline
Reference 49
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e937f9b1-052b-4c98-88d7-c1807eafc6d8 · outbound
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials write newline
Reference 50
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.