Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials | Paper Citation Record

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials write newline

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This paper cites J., Bambrick, J., et al.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Bambrick, J., et al

Reference 2

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

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This paper cites Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 4

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This paper cites J., De Jong, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., De Jong, W

Reference 5

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This paper cites B., Rodrigues, G.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials B., Rodrigues, G

Reference 6

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Observation f7b13618-053e-4e78-b4a0-8d785dd4f6fc · outbound

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

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 7

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This paper cites A foundation model for atomistic materials chemistry.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials A foundation model for atomistic materials chemistry

Reference 8

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This paper cites P., Musaelian, A., Simm, G.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Musaelian, A., Simm, G

Reference 9

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This paper cites P., Kornbluth, M., Molinari, N., Smidt, T.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Kornbluth, M., Molinari, N., Smidt, T

Reference 10

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 11

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This paper cites M., Ranu, S., and Krishnan, N.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials M., Ranu, S., and Krishnan, N

Reference 12

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 13

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This paper cites Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing

Reference 14

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 15

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials u gel, S., Br \

Reference 16

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 17

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This paper cites E., and Welling, M.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials E., and Welling, M

Reference 18

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This paper cites Does equivariance matter at scale?.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Does equivariance matter at scale?

Reference 19

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 20

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This paper cites B., et al.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials B., et al

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This paper cites G., Maley, S., Gibaldi, M., Simrod, S., Ogden, V., et al.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials G., Maley, S., Gibaldi, M., Simrod, S., Ogden, V., et al

Reference 22

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This paper cites Addressing the Band Gap Problem with a Machine-Learned Exchange Functional.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Addressing the Band Gap Problem with a Machine-Learned Exchange Functional

Reference 23

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This paper cites and Parrinello, M.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Parrinello, M

Reference 24

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This paper cites Open catalyst 2020 (oc20) dataset and community challenges.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Open catalyst 2020 (oc20) dataset and community challenges

Reference 25

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 26

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This paper cites and Ong, S.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Ong, S

Reference 27

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This paper cites H., Lo, A., Miret, S., Pate, B.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials H., Lo, A., Miret, S., Pate, B

Reference 28

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This paper cites F., Reid, A.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials F., Reid, A

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This paper cites On the Correlation Problem in Atomic and Molecular Systems.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials On the Correlation Problem in Atomic and Molecular Systems

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This paper cites J., Mori-S \'a nchez , P., and Yang, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Mori-S \'a nchez , P., and Yang, W

Reference 31

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This paper cites J., Mori-S \'a nchez , P., and Yang, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Mori-S \'a nchez , P., and Yang, W

Reference 32

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., and Ceder, G

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Matexpert: Decomposing materials discovery by mimicking human experts

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Htmd: high-throughput molecular dynamics for molecular discovery

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Torchmd: A deep learning framework for molecular simulations

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 37

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This paper cites A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

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This paper cites Phast: Physics-aware, scalable, and task-specific gnns for accelerated catalyst design.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Phast: Physics-aware, scalable, and task-specific gnns for accelerated catalyst design

Reference 39

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This paper cites Analyzing atomic interactions in molecules as learned by neural networks.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Analyzing atomic interactions in molecules as learned by neural networks

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This paper cites Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

Reference 41

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Lenssen, J

Reference 42

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials C., Soklaski, R., Axelrod, S., Samsi, S., Gomez-Bombarelli, R., Coley, C

Reference 43

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials A., Tadmor, E

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Force field optimization by end-to-end differentiable atomistic simulation

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This paper cites Searching for high-value molecules using reinforcement learning and transformers.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Searching for high-value molecules using reinforcement learning and transformers

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials L., Cococcioni, M., Dabo, I., et al

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials The non-linear nature of the cost of comprehensibility

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials B., Martiniani, S., and Miret, S

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Crystal design amidst noisy dft signals: A reinforcement learning approach

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials G., Zitnick, C

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This paper cites B., and Martiniani, S.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials B., and Martiniani, S

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Tibshirani, R

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials MESS: Modern Electronic Structure Simulations

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Coupled cluster finite temperature simulations of periodic materials via machine learning

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials E., Christensen, R., Dułak, M., Friis, J., Groves, M

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This paper cites K., Montoya, J.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials K., Montoya, J

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Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.833471Z

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Observation 95850f7f-9c46-48e7-86a1-8a25390e872b · outbound

This paper cites Difftaichi: Differentiable programming for physical simulation.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Difftaichi: Differentiable programming for physical simulation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.838117Z

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Observation cc9c4147-9c9f-46ea-872f-a285a385184c · outbound

This paper cites M., Yang, L., Linker, T., Olguin, M., Hattori, S., Luo, Y., Kalia, R.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials M., Yang, L., Linker, T., Olguin, M., Hattori, S., Luo, Y., Kalia, R

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.843841Z

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Observation 5b01175f-b156-4c09-8834-1611ad0730f4 · outbound

This paper cites J., Ong, S.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Ong, S

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.848087Z

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Observation 31967fb3-eb37-42f2-a17c-a5236c06859d · outbound

This paper cites P., Hautier, G., Chen, W., Richards, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Hautier, G., Chen, W., Richards, W

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.852741Z

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Observation 2ef87f09-e29e-4201-927d-7966c13f66b1 · outbound

This paper cites Space group constrained crystal generation.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Space group constrained crystal generation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.856972Z

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Observation 73ed7fa6-9ec5-406b-86a0-b54ed8567da1 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.861843Z

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Observation f50e14bd-99c6-4b64-8516-eb470a407170 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Highly accurate protein structure prediction with alphafold

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.866387Z

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Observation c326c6d4-614f-4a4c-ba7f-70a796dda3f1 · outbound

This paper cites Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.871022Z

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Observation a1aaa018-078b-45fc-99a4-869175f79f06 · outbound

This paper cites and Sham, L.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Sham, L

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.875687Z

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Observation 958beede-034a-4929-aac5-f41d21ad293f · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Neural operator: Learning maps between function spaces with applications to pdes

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.880184Z

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Observation a99c68bd-1745-4d64-8a79-2190376ac585 · outbound

This paper cites Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size

Reference 70

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T04:14:44.457869Z

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 28d43fe9-1922-438d-bf82-af46556f8516 · outbound

This paper cites and Hafner, J.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Hafner, J

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.888944Z

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Observation 20cdd5ef-fceb-43ce-b004-f557e6fe1fd3 · outbound

This paper cites u hne, T. D., Iannuzzi, M., Del Ben, M., Rybkin, V. V., Seewald, P., Stein, F., Laino, T., Khaliullin, R. Z., Sch \.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials u hne, T. D., Iannuzzi, M., Del Ben, M., Rybkin, V. V., Seewald, P., Stein, F., Laino, T., Khaliullin, R. Z., Sch \

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.893111Z

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Observation 185dd2b8-6f06-4e0f-aabb-a791ce077d7c · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.897520Z

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Observation 669c6d30-f3f5-4846-8199-85509f7c4f86 · outbound

This paper cites MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:41.948183Z

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Observation 63e3bd13-bdb5-4c58-8c61-a93088249167 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.032278Z

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Observation 5d57ea44-4457-4ec3-a005-903d8ca15ff3 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.051822Z

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Observation 36332a22-51af-4e37-bfc5-ea087e8a1f22 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.057343Z

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Observation f75abeb5-0313-47a7-9517-0644d0123c78 · outbound

This paper cites o rkman, T., Blaha, P., Bl \.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials o rkman, T., Blaha, P., Bl \

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.061798Z

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Observation 2564f160-0ad2-46f2-b413-4ef68d044f8e · outbound

This paper cites S., Kaba, S.-O., Zhu, Q., Galkin, M., Miret, S., and Ravanbakhsh, S.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials S., Kaba, S.-O., Zhu, Q., Galkin, M., Miret, S., and Ravanbakhsh, S

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:46.102531Z

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Observation e02e8b44-174d-4b3f-b27f-44069f736413 · outbound

This paper cites and Smidt, T.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials and Smidt, T

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.071446Z

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Observation 5dbce273-513d-43ad-921a-d573615f52ab · outbound

This paper cites Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.076561Z

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Observation db9b1b26-0329-4346-9e65-fc3f1ec83aa8 · outbound

This paper cites Intelligible models for classification and regression.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Intelligible models for classification and regression

Reference 82

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T04:14:44.230406Z

Source-reported events for the cited work

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Observation 7c422213-65a6-405b-8fc9-3a12cd8e9a89 · outbound

This paper cites Conceptual Problem with Calculating Electron Densities in Finite Basis Density Functional Theory.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Conceptual Problem with Calculating Electron Densities in Finite Basis Density Functional Theory

Reference 83

Resolution: verified exact; doi, observed 2026-08-09T04:14:42.760035Z

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

Observation 29740064-9659-4750-95d1-33b16586b6ee · outbound

This paper cites G., Bushmarinov, I.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials G., Bushmarinov, I

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.093395Z

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Observation ceaf8ba3-1aca-4e39-b36b-0372f3d48a20 · outbound

This paper cites S., Aykol, M., Cheon, G., and Cubuk, E.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials S., Aykol, M., Cheon, G., and Cubuk, E

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.098353Z

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Observation 91466f35-c3df-42c4-8297-71bae3257c60 · outbound

This paper cites Gradients are Not All You Need.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Gradients are Not All You Need

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.103084Z

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Observation c74f0175-226e-4135-b0e2-12baee1fd682 · outbound

This paper cites K., Chen, R.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials K., Chen, R

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:46.065898Z

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

Observation a5c8d57f-0e5a-4c84-9592-cbd2b0c3b89e · outbound

This paper cites Are LLMs Ready for Real-World Materials Discovery?.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Are LLMs Ready for Real-World Materials Discovery?

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.113062Z

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Observation 3e306419-064b-42ac-bb4c-1c2057391642 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:14:46.048912Z

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 66e47db9-cf83-4b6b-b66d-ec05ea04abcd · outbound

This paper cites A., Sanchez-Lengeling, B., Skreta, M., Venugopal, V., and Wei, J.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials A., Sanchez-Lengeling, B., Skreta, M., Venugopal, V., and Wei, J

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:46.032514Z

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

Observation 99c9c9c8-4ce7-4f14-a366-d78b24e48952 · outbound

This paper cites R., van Dijk , D., Wang, Z., Gigante, S., Burkhardt, D.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials R., van Dijk , D., Wang, Z., Gigante, S., Burkhardt, D

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.128205Z

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Observation 25a42366-60a5-4f36-8509-2402cb45d1bb · outbound

This paper cites J., Kornbluth, M., and Kozinsky, B.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials J., Kornbluth, M., and Kozinsky, B

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:46.016229Z

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 1484f640-967a-4ca2-92e7-08b41946c1b5 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.137624Z

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Observation 6c6f3844-28aa-4723-926b-48ea04677c6c · outbound

This paper cites M., Kuo, T.-S., Liu, Y., Dror, R., Brajovic, D., Yao, X., Bartolo, M., Rojas, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials M., Kuo, T.-S., Liu, Y., Dror, R., Brajovic, D., Yao, X., Bartolo, M., Rojas, W

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:45.993686Z

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

Observation ab0c5990-13bb-4fa2-810a-40c50c507987 · outbound

This paper cites P., Richards, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Richards, W

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:45.931283Z

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 6c5a1e88-3e04-4183-88c8-4788a6716bc0 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.199850Z

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Observation 3ebeecde-8c67-415d-9d20-590d88d05b96 · outbound

This paper cites P., Burke, K., and Ernzerhof, M.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Burke, K., and Ernzerhof, M

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.241249Z

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Observation c2fbce98-6512-43d9-8efd-2c51c5d388a6 · outbound

This paper cites P., Ruzsinszky, A., Tao, J., Staroverov, V.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials P., Ruzsinszky, A., Tao, J., Staroverov, V

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:14:45.799491Z

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 23b09e4f-3eb4-434a-84a6-e8c3feebb724 · outbound

This paper cites Matthews , A., and Foulkes, W.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Matthews , A., and Foulkes, W

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.251555Z

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Observation 10f68c17-6e3c-46ec-91ce-df2ac290aab8 · outbound

This paper cites an unresolved cited work.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Unresolved cited work

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.256921Z

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Pith citing papers