Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry | Paper Citation Record

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

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This paper cites Behler, Chemical Reviews121, 10037 (2021).

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Behler, Chemical Reviews121, 10037 (2021)

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Leimeroth, L

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Wines and K

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry H´ enin, T

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

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry A practical guide to machine learning interatomic potentials -- Status and future

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This paper cites Batzner, A.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Batzner, A

Reference 11

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This paper cites Batatia, D.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Batatia, D

Reference 12

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This paper cites Thrun, inAdvances in Neural Information Processing Systems, Vol.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Thrun, inAdvances in Neural Information Processing Systems, Vol

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Ramakrishnan, P

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Nandi, C

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry O’Neill, B

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This paper cites Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

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This paper cites Novelli, G.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Novelli, G

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This paper cites The Good, the Bad, and the Ugly of Atom- istic Learning for.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry The Good, the Bad, and the Ugly of Atom- istic Learning for

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This paper cites Fine-tuning universal machine-learned interatomicpotentials: Atutorialonmethodsandapplications.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Fine-tuning universal machine-learned interatomicpotentials: Atutorialonmethodsandapplications

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Pitfield, M.-P

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Chanussot, A

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Chen and S

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Batatia, P

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Uma: A family of universal models for atoms

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Cheng, G

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Kapil, C

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Qamar, M

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Buciluˇ a, R

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Distilling the Knowledge in a Neural Network

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Cattin, T

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces

Reference 46

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Distillation of atomistic foundation models across architectures and chemical domains

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Magd˘ au, D

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Montero de Hijes, C

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Holten, C

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Grimme, J

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Yeh and G

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Kapil, A

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Andreani, G

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Pietropaolo, R

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Ceriotti, J

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Palos, E

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 65

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Observation 2b758199-a78c-4dfc-be60-22078b1e1a64 · outbound

This paper cites Marsalek and T.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Marsalek and T

Reference 66

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Observation d8ed8596-a886-4d16-b492-2ca471153a38 · outbound

This paper cites Cheng, J.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Cheng, J

Reference 67

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This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 68

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Observation 70a4d94e-ae56-444d-ab0f-ee9e8a666ae8 · outbound

This paper cites Fujishima and K.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Fujishima and K

Reference 69

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This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 70

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Observation 94f028df-2e98-47e3-b940-28523783c92e · outbound

This paper cites Zhang, B.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Zhang, B

Reference 71

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This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 72

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Observation 190c207e-2b16-4f60-b4f5-702238c91954 · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 73

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Observation 61a293d6-8d12-433d-9abe-c4a58a6177b5 · outbound

This paper cites Jafari, B.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Jafari, B

Reference 74

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Observation eca9969d-ba0e-4a67-8630-5ee7d0293a2d · outbound

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Kumar, N

Reference 75

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Observation 5605863e-914c-4829-8658-ca5110839460 · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 76

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Observation 98b718f6-b4fd-4d85-a7a0-348ffaa955b7 · outbound

This paper cites Diebold, Surface Science Reports48, 53 (2003).

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Diebold, Surface Science Reports48, 53 (2003)

Reference 77

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Observation a2f41c88-a100-43d7-bf2b-50748d0289e9 · outbound

This paper cites Ketteler, S.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Ketteler, S

Reference 78

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Observation 57f37130-0746-43c8-a989-c44e001d143a · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 79

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Observation c2a3ec04-0ae7-4a0d-b1bc-1869d1323af2 · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 80

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Observation e292d4a4-1ac2-4bd7-88d5-d6e3a1ff62bd · outbound

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Jla-gardner/augment-atoms,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation e97f90c1-81b2-4f5b-a432-c3cf0715ac1d · outbound

This paper cites Ceriotti, W.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Ceriotti, W

Reference 82

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Observation 845d6319-ef89-42ad-bd4d-9f40315b1b0e · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 83

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Observation be4ed7ad-762f-4125-896a-354373372984 · outbound

This paper cites Litman, D.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Litman, D

Reference 84

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Observation a2fc3357-142a-4c65-9be2-2b8ffb7dcb2e · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 85

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Observation b50c79a1-def8-4a9c-8786-8195000664bc · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 86

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Observation ca835c90-4c25-4d19-9649-87b5d5943a18 · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation 4b5eacb9-85e7-477f-8484-ad71708bc4ff · outbound

This paper cites an unresolved cited work.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Unresolved cited work

Reference 88

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Observation 1ed426f0-1767-4b37-bbcd-c91ac84d2b08 · outbound

This paper cites Marx, ChemPhysChem7, 1848 (2006).

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Marx, ChemPhysChem7, 1848 (2006)

Reference 89

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Observation ccbf5e2c-c7c1-4860-9477-0b1146a2fe5e · outbound

This paper cites Zhang and W.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Zhang and W

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation 87af5a15-1be0-44e5-92f9-807b49127491 · outbound

This paper cites Fast-group-cam/data CC water,.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Fast-group-cam/data CC water,

Reference 91

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Observation dfdd2f5c-7cac-42a2-b52e-1372fe431bbc · outbound

This paper cites Klimeˇ s, D.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Klimeˇ s, D

Reference 92

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Observation 3827982b-f7a0-4b56-a7cd-05713efc17f7 · outbound

This paper cites BingqingCheng/TiO2-water,.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry BingqingCheng/TiO2-water,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation bcf6de37-b5ad-43e5-a28b-e30541a5571c · outbound

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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry ACEsuit/mace-foundations,

Reference 94

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Observation 13937848-d727-4983-ba35-a39fc4ec2720 · outbound

This paper cites Batatia, S.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Batatia, S

Reference 95

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation bfbd01d5-5354-425c-a27a-eeb14a06db02 · outbound

This paper cites ACEsuit/mace,.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry ACEsuit/mace,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation 86d5098b-4e22-42f1-8cf4-4f2909b972ee · outbound

This paper cites Drautz, Physical Review B99, 014104 (2019).

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Drautz, Physical Review B99, 014104 (2019)

Reference 97

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation 451a657f-be8e-43b7-ae16-6afc0b598ee0 · outbound

This paper cites ICAMS/python-ace,.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry ICAMS/python-ace,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

Source-reported events for the cited work

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Observation 919d401a-5120-4de5-8a2c-7a1fbf59d393 · outbound

This paper cites Bochkarev, Y.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Bochkarev, Y

Reference 99

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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Observation 8df97880-8221-4747-ae59-ff9b1da90e9f · outbound

This paper cites Lysogorskiy, C.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Lysogorskiy, C

Reference 100

Resolution: unresolved; no resolver link, observed 2026-06-27T20:50:41.513859Z

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