Paper Citation Record · LEDGER

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials

As of 9 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 1 inbound Pith citation observation for arXiv:2502.07104.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.07104 v2

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measured 76 of 76 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 9c1e32fc-5e37-4aff-8d17-763715d85cce · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 1

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 2

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 3

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 4

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Observation 77415443-8328-466f-8b2d-25a737dedb19 · outbound

This paper cites Behler, The Journal of chemical physics 145 (2016).

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Behler, The Journal of chemical physics 145 (2016)

Reference 5

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 6

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Observation 9ebeb34d-a2ac-4c34-ae11-f74cd878a7b1 · outbound

This paper cites Mishin, Acta Materialia 214, 116980 (2021).

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Mishin, Acta Materialia 214, 116980 (2021)

Reference 7

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This paper cites Stocker, J.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Stocker, J

Reference 8

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This paper cites Lysogorskiy, C.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Lysogorskiy, C

Reference 9

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This paper cites Mortazavi, X.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Mortazavi, X

Reference 10

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Observation 897fad1e-3e96-403a-b2e2-7661a9b63713 · outbound

This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 11

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 12

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This paper cites Montes de Oca Zapiain, M.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Montes de Oca Zapiain, M

Reference 13

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This paper cites Alexander, A.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Alexander, A

Reference 14

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This paper cites Gavini, S.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Gavini, S

Reference 15

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 16

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This paper cites Hegde, E.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Hegde, E

Reference 17

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 18

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This paper cites Zaverkin, D.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Zaverkin, D

Reference 19

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This paper cites Longbottom and P.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Longbottom and P

Reference 20

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This paper cites Kulichenko, K.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Kulichenko, K

Reference 21

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Kurniawan, C

Reference 22

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This paper cites Hegde, E.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Hegde, E

Reference 23

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 25

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

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This paper cites Improved uncertainty quantification for Gaussian process regression based interatomic potentials.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Improved uncertainty quantification for Gaussian process regression based interatomic potentials

Reference 29

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 30

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This paper cites DEUP: Direct Epistemic Uncertainty Prediction.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials DEUP: Direct Epistemic Uncertainty Prediction

Reference 31

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This paper cites Exploring parameter dependence of atomic minima with implicit differentiation.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Exploring parameter dependence of atomic minima with implicit differentiation

Reference 32

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This paper cites Masegosa, Advances in Neural Information Processing Systems 33, 5479 (2020).

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Masegosa, Advances in Neural Information Processing Systems 33, 5479 (2020)

Reference 33

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This paper cites Swinburne and D.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Swinburne and D

Reference 34

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This paper cites Imbalzano, Y.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Imbalzano, Y

Reference 35

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 36

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This paper cites A foundation model for atomistic materials chemistry.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials A foundation model for atomistic materials chemistry

Reference 37

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 38

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 39

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 40

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Observation ceb252f5-0c61-422d-8a8d-8b396550e8df · outbound

This paper cites Vandermause, S.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Vandermause, S

Reference 41

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This paper cites On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials

Reference 42

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Blondel, Q

Reference 43

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 44

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Artrith and J

Reference 45

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This paper cites Zaverkin and J.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Zaverkin and J

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.700536Z

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This paper cites Schran, K.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Schran, K

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.691595Z

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Observation a03447c8-f632-4029-9366-fc2fa81a84c9 · outbound

This paper cites Breiman, Machine learning 24, 123 (1996).

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Breiman, Machine learning 24, 123 (1996)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.652478Z

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This paper cites Kellner and M.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Kellner and M

Reference 49

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Observation 69a4cabd-0642-4c34-a839-e01c9dc6f5d4 · outbound

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:44.002937Z

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This paper cites Lakshminarayanan, A.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Lakshminarayanan, A

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.669056Z

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Observation 605c3873-dd93-4908-825d-215423b193d9 · outbound

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.660895Z

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This paper cites Kahle and F.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Kahle and F

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.643136Z

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Observation 5412862a-b0f9-4736-95f9-edb81ac178d9 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 54

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Observation 1c5cbcd9-c6c9-4e35-8325-bbab1a2931de · outbound

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Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.624761Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6574236d-3019-48d8-a607-54cce413329e · outbound

This paper cites Williams, K.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Williams, K

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.615730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3eb9292d-db07-4677-93a1-62b6f5cc3e99 · outbound

This paper cites Goan and C.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Goan and C

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.607738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4dc5ee53-5988-4549-95e8-2c6c77217fdc · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.599777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d841a040-bf1a-405b-b2da-b241bc1f27ab · outbound

This paper cites Sargsyan, X.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Sargsyan, X

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.591248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f90d7f59-8b7e-4e40-a2af-75e5629075fc · outbound

This paper cites A view on model misspecification in uncertainty quantification.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials A view on model misspecification in uncertainty quantification

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:51:43.408874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e536419b-dd50-4aa5-92a2-e935c8b422a0 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.583123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b3817f5-10a8-4799-9206-41a273c1405d · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.574463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation affaca53-20ba-410a-9782-5ead3ecacf92 · outbound

This paper cites Kleijn and A.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Kleijn and A

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.565644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65b5ceef-d44c-462c-8f17-9fe6094cd816 · outbound

This paper cites Pedregosa, G.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Pedregosa, G

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.556941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdd8500c-7367-4064-a255-b9b5cafca7f5 · outbound

This paper cites Germain, F.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Germain, F

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.548495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdf6e9ad-5adf-4d1a-9c95-a6b63fd709a4 · outbound

This paper cites Hoeffding, The collected works of Wassily Hoeffding , 409 (1994).

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Hoeffding, The collected works of Wassily Hoeffding , 409 (1994)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.539802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64755d3d-b015-461a-86ea-d56ee69b2a43 · outbound

This paper cites User-friendly introduction to PAC-Bayes bounds.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials User-friendly introduction to PAC-Bayes bounds

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T13:51:43.300906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b0a6590-a58d-49f0-bf57-2cbe4809b6e2 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.531379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 111c10cd-e9e3-43fe-affe-bbcbc5ccbba1 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:51:43.522939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b780970-f404-45d1-9293-057ba9422474 · outbound

This paper cites Karabin and D.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Karabin and D

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.512907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14d0373e-de33-4895-a7b8-97d4640094dc · outbound

This paper cites Montes de Oca Zapiain, M.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Montes de Oca Zapiain, M

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.503677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 251ff1a8-4717-4b36-b86a-4475c5eda251 · outbound

This paper cites Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:51:43.388450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a48d2a2-71a0-4a4d-95e6-1f082e59bcc9 · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T13:51:43.320927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ded790ce-ede0-4026-846e-ff46b5fb3741 · outbound

This paper cites Bochkarev, Y.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Bochkarev, Y

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.494463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 071efb1d-3c82-40c8-9d30-b6a4efe2b9dd · outbound

This paper cites Schmidt, N.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Schmidt, N

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.485805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 22f384d1-1f47-4f26-87dd-fbf1465d2be6 · outbound

This paper cites Drautz, Phys.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Drautz, Phys

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:51:43.476246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials cites this paper.

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

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:31:09.887520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.