Paper Citation Record · LEDGER

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials

As of 9 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.

pith.paper-citation-record.v1
2508.03405 v1

Coverage vector

measured 50 of 50 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T04:31:09.370859Z

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

50 of 50 outbound references displayed

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Outbound references

Observation 4640c514-04ff-4e6b-a12c-4851585398be · outbound

This paper cites Friederich, F.

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

This paper cites Ceriotti, Beyond potentials: Integrated machine learning models for materials.

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

This paper cites Zuo, et al., Performance and Cost Assessment of Machine Learning Interatomic Potentials.

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

This paper cites Jacobs, et al., A practical guide to machine learning interatomic potentials--Status and future.

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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Observation f16c2011-dc98-4db7-8c53-333322f42f55 · outbound

This paper cites Qamar, M.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Qamar, M

Reference 5

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Observation 51d07068-7db2-4bff-b2df-971b00b7a3ee · outbound

This paper cites Liang, et al., Atomic cluster expansion for Pt--Rh catalysts: From ab initio to the simulation of nanoclusters in few steps.

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

This paper cites an unresolved cited work.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 7

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 8

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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

This paper cites Podryabinkin, K.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Podryabinkin, K

Reference 10

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Observation 6f176824-f930-4005-9e94-e48b469911f0 · outbound

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

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

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Observation 01b71c52-b196-4716-a44a-1c022e5d0f13 · outbound

This paper cites Lysogorskiy, et al., Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon.

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

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Observation 079c642a-4fc7-4205-9cfc-1495136238ca · outbound

This paper cites Bochkarev, et al., Efficient parametrization of the atomic cluster expansion.

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

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Observation e7cc17ca-b0ae-481e-adeb-4b457eb67842 · outbound

This paper cites Xu, et al., GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials.

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

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 15

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Observation b56b9af5-1379-4639-89b2-7246638d5b86 · outbound

This paper cites an unresolved cited work.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 16

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Observation fd94f14b-3301-4e86-a84e-05b8b4c03c84 · outbound

This paper cites Rybin, I.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Rybin, I

Reference 17

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Observation 1f5bc08e-51a7-43fa-a292-b3ad7114b814 · outbound

This paper cites Klimanova, N.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Klimanova, N

Reference 18

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Observation d05b1135-b91d-4b36-bd49-345cc6c8ab41 · outbound

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 19

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Observation 9364d76e-2f3e-4f8c-9eef-3d9ccdc41229 · outbound

This paper cites van der Oord, M.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials van der Oord, M

Reference 20

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Observation ee27fd09-fb5b-4016-94ff-161f81329b12 · outbound

This paper cites Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials

Reference 21

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Observation 25a05d9c-9a88-4ba3-9d5e-68050232762b · outbound

This paper cites an unresolved cited work.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 22

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Observation 1fd60f50-d4a5-4a26-86df-de431095a078 · outbound

This paper cites Lysogorskiy, A.

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

This paper cites Shuang, et al., Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction.

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

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Observation 8fe9bc81-4423-427d-b29f-6c0ec64f60dc · outbound

This paper cites Batatia, D.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Batatia, D

Reference 25

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Observation cef73eb7-7821-4fa8-b0c3-d32d1c45a739 · outbound

This paper cites Zhang, G.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Zhang, G

Reference 26

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This paper cites Byggm \"a star, K.

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Byggm \"a star, K

Reference 27

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This paper cites NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements.

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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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 29

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Hodapp, A

Reference 31

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Mismetti, M

Reference 32

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 33

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Schwalbe-Koda, S

Reference 34

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 35

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This paper cites A foundation model for atomistic materials chemistry.

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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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

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This paper cites Drautz, Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer.

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

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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

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Thompson, L

Reference 40

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 42

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Kresse, J

Reference 43

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 45

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 46

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials Unresolved cited work

Reference 47

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Observation c6bee55b-9454-46fd-8aba-39b0c9ef3594 · outbound

This paper cites Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool.

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

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials , " * write output.state after.block = add.period write newline

Reference 49

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Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials write newline

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Pith citing papers

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