Six Open Questions in Machine-Learned Interatomic Potential Foundation Models | Paper Citation Record

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This paper cites Metropolis, A.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Metropolis, A

Reference 1

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This paper cites Biometrika , year = 1970, month = apr, volume =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Biometrika , year = 1970, month = apr, volume =

Reference 2

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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This paper cites experiments.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models experiments

Reference 4

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This paper cites Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Exploiting the isomorphism between quantum theory and classical statistical mechanics of polyatomic fluids

Reference 5

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This paper cites Parrinello and A.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Parrinello and A

Reference 6

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This paper cites Series A, Containing Papers of a Mathematical and Physical Character106463–477 ISSN 2053-9150 URL http://dx.doi.org/10.1098/rspa.1924.0082.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Series A, Containing Papers of a Mathematical and Physical Character106463–477 ISSN 2053-9150 URL http://dx.doi.org/10.1098/rspa.1924.0082

Reference 8

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Reference 9

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This paper cites Generalized neural-network representation of high-dimensional potential-energy surfaces.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Generalized neural-network representation of high-dimensional potential-energy surfaces

Reference 10

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This paper cites On representing chemical environments.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models On representing chemical environments

Reference 11

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This paper cites Drautz, Atomic cluster expansion for accurate and transferable interatomic po- tentials, Phys.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Drautz, Atomic cluster expansion for accurate and transferable interatomic po- tentials, Phys

Reference 12

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This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 14

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This paper cites and Benjamin Kurt Miller and Wouter Boomsma and Bradley Dice and Kostiantyn Lapchevskyi and Maurice Weiler and Micha.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models and Benjamin Kurt Miller and Wouter Boomsma and Bradley Dice and Kostiantyn Lapchevskyi and Maurice Weiler and Micha

Reference 15

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

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models On the Opportunities and Risks of Foundation Models

Reference 17

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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This paper cites Transformers discover molecular structure without graph priors.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Transformers discover molecular structure without graph priors

Reference 19

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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This paper cites Mater.10300 URL https://doi.org/10.1038/s41524-024-01486-1.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Mater.10300 URL https://doi.org/10.1038/s41524-024-01486-1

Reference 21

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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This paper cites Elena, Sam Walton Norwood, Thomas Wolf, and Gábor Csányi.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Elena, Sam Walton Norwood, Thomas Wolf, and Gábor Csányi

Reference 24

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This paper cites Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials,.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials,

Reference 25

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This paper cites Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

Reference 26

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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This paper cites Comparing the latent features of universal machine-learning interatomic potentials.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Comparing the latent features of universal machine-learning interatomic potentials

Reference 28

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This paper cites Univer- sally converging representations of matter across scientific foundation models.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Univer- sally converging representations of matter across scientific foundation models

Reference 29

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E

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This paper cites Equivariant Symmetry Breaking Sets.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Equivariant Symmetry Breaking Sets

Reference 35

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Relaxed Equivariant Graph Neural Networks

Reference 36

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Orb-v3: atomistic simulation at scale

Reference 37

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Mater.11178 URLhttps://doi.org/10.1038/s41524-025-01650-1

Reference 40

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This paper cites Scaling Laws and Symmetry, Evidence from Neural Force Fields.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Scaling Laws and Symmetry, Evidence from Neural Force Fields

Reference 41

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Does equivariance matter at scale?

Reference 42

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This paper cites PET-MAD, a lightweight universal interatomic potential for advanced materials modeling.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

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This paper cites User-friendly tail bounds for sums of random matrices.Foundations of computational mathematics, 12(4):389–434.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models User-friendly tail bounds for sums of random matrices.Foundations of computational mathematics, 12(4):389–434

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This paper cites Accessed 2025-11-10 URLhttps://github.com/NVIDIA/cuEquivariance.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Accessed 2025-11-10 URLhttps://github.com/NVIDIA/cuEquivariance

Reference 47

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models TorchScript is deprecated; usetorch.export

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models arXiv preprint arXiv:2506.02023 , year=

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Decoupled Weight Decay Regularization

Reference 52

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 53

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This paper cites A universal graph deep learning interatomic potential for the periodic table , volume =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models A universal graph deep learning interatomic potential for the periodic table , volume =

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Mater.11 1–11 URLhttps://doi.org/10.1038/s41524-025-01727-x

Reference 56

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This paper cites arXiv preprint arXiv:2505.01590 , year=.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models arXiv preprint arXiv:2505.01590 , year=

Reference 57

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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 58

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Observation 5ce819ce-8ce3-4823-9889-ff1cc7d4970c · outbound

This paper cites Engel and Jörg Behler and Christoph Dellago and Michele Ceriotti , title =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Engel and Jörg Behler and Christoph Dellago and Michele Ceriotti , title =

Reference 59

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.831061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0182b5e-3763-4205-a412-7dfc4d051137 · outbound

This paper cites Commun.114895 URL https://doi.org/10.1038/s41467-020-18556-9.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Commun.114895 URL https://doi.org/10.1038/s41467-020-18556-9

Reference 60

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.681552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e959059b-25c9-480b-bffa-a4fcfe5107bf · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-07-02T19:37:19.141044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f0a7a7f-60cd-4957-9555-62a3be59dc93 · outbound

This paper cites 2024 , issn =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models 2024 , issn =

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T21:31:16.672755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation daa1c0ba-4751-4ef8-ba3e-2a2f82ea9181 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-06-27T21:31:16.829495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1902fd34-7ec7-489a-a4d0-af2420ab555c · outbound

This paper cites Liu , R.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Liu , R

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.833533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 34cf8013-a6f7-41a2-9f10-ebb3cd11c774 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 084f6d5c-0df3-4daa-9a1b-b2d11308b493 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4779636b-c536-474e-bc6f-9ab48fb05e82 · outbound

This paper cites Choudhary, B.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Choudhary, B

Reference 67

Resolution: metadata mismatch; doi, observed 2026-06-27T21:31:16.667324Z

Source-reported events for the cited work

correction dated 2022-10-28. Source: crossref record 10.1038/s41524-022-00913-5->10.1038/s41524-021-00650-1:correction, observed 2026-07-11T02:59:00.188458+00:00. This notice travels one citation hop only.

Observation 3657a728-de6f-4802-ba44-1e1b2e6b1b53 · outbound

This paper cites When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.875234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a626ac6-ea5f-4177-924e-cdffa025c620 · outbound

This paper cites Deep Double Descent: Where Bigger Models and More Data Hurt.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.825321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ef66ac4d-795e-410b-a81c-98570e9f7091 · outbound

This paper cites Scaling Laws for Neural Language Models.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Scaling Laws for Neural Language Models

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-07-02T19:37:19.076048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fb50074-166d-4ac9-a512-01229dd4aaa3 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 71

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.872954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 67069e94-527d-433b-a36c-cb55ea3e80e9 · outbound

This paper cites How accurate are dft forces? unexpectedly large uncertainties in molecular datasets.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models How accurate are dft forces? unexpectedly large uncertainties in molecular datasets

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.670274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a7b4573-b00a-401d-a2fe-3cf4b40fd3af · outbound

This paper cites and Burke, Kieron and Ernzerhof, Matthias , month = oct, year =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models and Burke, Kieron and Ernzerhof, Matthias , month = oct, year =

Reference 73

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.822910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 32b8437d-1e19-4732-9974-e5cf7a4cc36e · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 74

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.826971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dbbefa88-1940-4d13-bbbd-abf5f0fb2fdc · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 75

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.835140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a56024be-8855-4772-ae55-1fab9dbe7e6d · outbound

This paper cites Stephens, F.J.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Stephens, F.J

Reference 76

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.843704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3e49db84-f406-4b41-85d5-b7aa8c1ac9b0 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 77

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.864743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba2d5bdf-43fc-41d6-acd5-c2a44798acfe · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 191ec742-92c9-49a9-bf76-5b3eb30e566c · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.869822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e62fea5-2d79-41d4-be40-404b3e32818f · outbound

This paper cites Mater.11URL https://doi.org/10.1038/s41524-025-01550-4.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Mater.11URL https://doi.org/10.1038/s41524-025-01550-4

Reference 80

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.871424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 24e1e125-032f-4209-abd8-a30b1aefb936 · outbound

This paper cites Learn.: Sci.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Learn.: Sci

Reference 81

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.680024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77491a41-ceee-4211-8cd2-ba78a934f5d6 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 925f6c6d-2d33-41bb-8d98-713cb70371e0 · outbound

This paper cites Kohn ,\ title title Density functional and density matrix method scaling linearly with the number of atoms , \ https://doi.org/10.1103/PhysRevLett.76.3168 journal journal Phys.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Kohn ,\ title title Density functional and density matrix method scaling linearly with the number of atoms , \ https://doi.org/10.1103/PhysRevLett.76.3168 journal journal Phys

Reference 83

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.674904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74b48d34-3bab-453a-bc25-27206c18fd44 · outbound

This paper cites Prodan \ and\ author W.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Prodan \ and\ author W

Reference 84

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.814683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57aee116-2902-47cb-beef-5700b1fa5d57 · outbound

This paper cites Grisafi \ and\ author M.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Grisafi \ and\ author M

Reference 85

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.816275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9441271-6ce0-4a06-a944-c7439892369d · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 86

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.867522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 966e729d-8cb9-4605-9f8c-26fab7cbf39c · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 87

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.866133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ab47ec4-ea46-4bb4-9905-a8702aecc8ff · outbound

This paper cites Finkler, Stefan Goedecker, and Jörg Behler.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Finkler, Stefan Goedecker, and Jörg Behler

Reference 88

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.811471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1574bf08-5edc-4f5d-b181-93460166cb74 · outbound

This paper cites Energy Mater.412562–12569 URLhttps://doi.org/10.1021/acsaem.1c02363.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Energy Mater.412562–12569 URLhttps://doi.org/10.1021/acsaem.1c02363

Reference 89

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.678480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 002f321c-2849-4ecf-9f6d-e721b5722dce · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2a096da-7317-406f-b4ed-2d39df30e06f · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 91

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.676752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e4fe6009-56db-405a-98a2-73e121747b0d · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 92

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.809874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1cb20e99-bd69-414a-848d-4ad9ac9cc7e9 · outbound

This paper cites Rumiantsev, M.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Rumiantsev, M

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.178395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d11e8058-ce68-4e67-a3ce-29d338cc949c · outbound

This paper cites Unke, Stefan Chmiela, Michael Gastegger, Kristof T.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unke, Stefan Chmiela, Michael Gastegger, Kristof T

Reference 94

Resolution: metadata mismatch; doi, observed 2026-06-27T21:31:16.813006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50c82254-d0b2-4513-bed4-a24c9ab9f6c7 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 95

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0945da52-4c85-4e21-9c8e-2d63ff505687 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 96

Resolution: unresolved; no resolver link, observed 2026-06-27T21:27:50.941166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c05ef8a6-d3e5-403a-a9e4-59091385fdc0 · outbound

This paper cites So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.196570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 45402d9c-455a-4059-9301-c1ab0728d22c · outbound

This paper cites Equivariant Matrix Function Neural Networks.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Equivariant Matrix Function Neural Networks

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.116541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 897e8d76-433e-49dc-b13d-e928e8181d0f · outbound

This paper cites Physica Status Solidi (B) , author =.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Physica Status Solidi (B) , author =

Reference 99

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.817934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b5d05c3-b606-4dbd-a036-e067a8b2af01 · outbound

This paper cites an unresolved cited work.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Unresolved cited work

Reference 100

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.699568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0560c4a3-0111-4291-8204-48ef336d27b4 · outbound

This paper cites Resta, Macroscopic polarization in crystalline dielectrics: the geometric phase approach, Rev.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Resta, Macroscopic polarization in crystalline dielectrics: the geometric phase approach, Rev

Reference 101

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.697553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99a9f402-df64-43d0-aaac-53ee784427ec · outbound

This paper cites Gonze \ and\ author C.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Gonze \ and\ author C

Reference 102

Resolution: verified exact; doi, observed 2026-06-27T21:31:16.802341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers