Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems | Paper Citation Record

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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This paper cites Classical Trajectories Using the Full Ab Initio Potential Energy Surface H −+CH4 →CH 4+H−.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Classical Trajectories Using the Full Ab Initio Potential Energy Surface H −+CH4 →CH 4+H−

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unified Approach for Molecular Dynamics and Density-Functional Theory

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems B.; Millam, J

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Recent Advances and Perspectives on Nonadiabatic Mixed Quantum-Classical Dynamics

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Escaping Free-Energy Minima

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This paper cites Efficient Explo- ration of Reactive Potential Energy Surfaces Using Car- Parrinello Molecular Dynamics.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Efficient Explo- ration of Reactive Potential Energy Surfaces Using Car- Parrinello Molecular Dynamics

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Petersen, M

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems T.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems R.; Moore, D

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems M.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems B.; Raghavachari, K.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems M.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems E.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Day, T

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems The Quest for a Universal Den- sity Functional: The Accuracy of Density Functionals Across a Broad Spectrum of Databases in Chemistry and Physics

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Perspective: Advances and challenges in treating van der Waals dispersion forces in density functional theory

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems B.; Schaefer, H

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems J.; Mori-S´ anchez, P.; Yang, W

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems E.; Poltavsky, I.; Schuett, K

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems B.; Batzner, S.; Xie, Y.; Sun, L.; Kolpak, A

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems P.; Simm, G

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Generalized neural-network representation of high-dimensional potential-energy sur- faces

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Isayev, O.; Roitberg, A

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Four Generations of High-Dimensional Neu- ral Network Potentials

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Transferability of ma- chine learning potentials: Protonated water neural net- work potential applied to the protonated water hexamer

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Riley, P

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Freeman, W

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems C.; Zhu, X.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems C.; Haycraft, C.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems C.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems N.; Kaiser, L

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Ricard, T

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Saha, D.; Dwivedi, A.; Lopez-Ruiz, M

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems On-the-fly

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems on-the-fly

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems C.; Kumar, A.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems H.; Ricard, T

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems H.; Iyengar, S

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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This paper cites S.; Zhang, J.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Zhang, J

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Observation c6365294-239f-4c15-a053-5a5d9a399670 · outbound

This paper cites K.; Shah, N.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems K.; Shah, N

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Observation c1c6a4aa-0a45-4df0-919e-ee39a34b83d7 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems C.; Franzosa, R

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Observation 77683c0b-4789-49f7-b449-c149f9b3b6ef · outbound

This paper cites 20 Affine spaces.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems 20 Affine spaces

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Observation 4bcd0218-6615-4961-9923-fa4b6b546be5 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Observation 1037ffdf-f859-4ec0-a66b-0a3ae0253f3d · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems IMOMM: A new integrated ab initio + molecular mechanics geometry optimization scheme of equilibrium structures and transition states

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Observation ac185988-f7f0-4f05-9ec5-deb549a245b3 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems L.; Conte, R.; Bow- man, J

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Observation e2a81670-e14b-4d4e-a455-594d0ede6fea · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems O.; Rupp, M.; von Lilien- feld, O

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Observation 4b1c1795-9923-4b78-92b6-b58e4e22fcef · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems ∆-Machine learning- driven discovery of double hybrid organic–inorganic per- ovskites

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Observation a2b53cfe-1ce9-469a-9bf6-7c747df4f9c6 · outbound

This paper cites A.; Greenwell, C.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems A.; Greenwell, C

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Observation bcef2ee7-528e-45c3-93f0-8fe685e491db · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems L.; Qu, C.; Yu, Q.; Conte, R.; Tkatchenko, A.; Bowman, J

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:27.556821Z

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Observation e04642f8-a49f-4e90-b302-fb3940390579 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Neural Machine Translation by Jointly Learning to Align and Translate

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:27.680878Z

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Observation 73f52123-c864-48d2-892d-2901d36faf0e · outbound

This paper cites S.; Voth, G.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Voth, G

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Observation c902a7cc-7901-4901-ab31-248691e6901d · outbound

This paper cites Hydration and Intermolecular Interaction: Infrared Investigations with Polyelectrolyte Membranes; Academic Press, 2012.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Hydration and Intermolecular Interaction: Infrared Investigations with Polyelectrolyte Membranes; Academic Press, 2012

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:27.979796Z

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Observation ef446d57-d5c4-4787-b079-48f4178a7e81 · outbound

This paper cites E.; Ungar, P.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems E.; Ungar, P

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Observation 475eeb1b-e355-470a-acc4-1e9c53be2219 · outbound

This paper cites an unresolved cited work.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

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Observation 43b8b1d9-ea34-4037-9ff8-a6f06b0519a1 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems W.; Voth, G

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Observation 2c201814-f120-4b27-941d-21a65e8c8122 · outbound

This paper cites W.; Voth, G.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems W.; Voth, G

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Observation 56bb1665-1483-48ac-8ba5-1a99ce23cac5 · outbound

This paper cites Dynamics and Infrared Spectroscopy of the Protonated Water Dimer.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Dynamics and Infrared Spectroscopy of the Protonated Water Dimer

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Observation d8c1a1d5-86f5-40f9-8bc4-37ed8e579517 · outbound

This paper cites Structure and Dynamics of a Proton Wire: A Theoretical Study ofH + Translocation Along the Single-File Water Chain in the Gramicidin a Chan- nel.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Structure and Dynamics of a Proton Wire: A Theoretical Study ofH + Translocation Along the Single-File Water Chain in the Gramicidin a Chan- nel

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Observation 0cfb51a3-3804-49aa-99ea-033edf761514 · outbound

This paper cites J.; Phillips, L.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems J.; Phillips, L

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:29.204558Z

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Observation 7740363d-c387-4ee8-a8e6-f4c5a5a3c4ac · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

Reference 79

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Observation 5a9ba27b-4278-44f7-89d1-140c2c409d21 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Theoretical study of H+ transloca- tion along a model proton wire

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Observation bb7bd433-e49f-4d74-8d31-5154d69a4ca1 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Solva- tion and Hydrogen-Bonding Effects on Proton Wires

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Observation 7c263f61-75d8-4402-9b69-11496706eb63 · outbound

This paper cites L.; Schmitt, U.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems L.; Schmitt, U

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:29.859380Z

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Observation 3160a42f-dfa0-4a9e-84a6-4f50c61e3ac2 · outbound

This paper cites Water structure of a hydrophobic protein at atomic resolution: Pentagon rings of water molecules in crystals of crambin.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Water structure of a hydrophobic protein at atomic resolution: Pentagon rings of water molecules in crystals of crambin

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Observation 8025fe16-c0a0-47ba-9cc7-949b4bd425fc · outbound

This paper cites M.; Shieh, H.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems M.; Shieh, H

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:30.097519Z

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Observation ae3a58ac-bc3f-4ac5-b69a-22214240ede9 · outbound

This paper cites A.; Peek, M.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems A.; Peek, M

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Observation 41a7080d-e0c4-41b1-bf56-b15cea7b3c57 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems S.; Tripp, B

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Observation edae8d32-a30b-4de9-ab81-5867667a59b1 · outbound

This paper cites The Grotthuss Mechanism.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems The Grotthuss Mechanism

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Observation 8fcbd020-c996-410a-8a35-784d59a6e892 · outbound

This paper cites A.; Wong, M.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems A.; Wong, M

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Observation ecf69e7c-95d4-4363-a071-e383d8b36439 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Some methods for classification and anal- ysis of multivariate observations

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Observation e319eb1b-a2e8-42dd-9dc6-7ee72d740a57 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Computing Dirichlet tessellations

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Observation 895484b3-628f-4c1e-a4fd-535dbce5d9a2 · outbound

This paper cites Voronoi Diagrams — A survey of a fundamental geometric data structure.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Voronoi Diagrams — A survey of a fundamental geometric data structure

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:30.907994Z

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Observation c1ce0c20-252e-4e65-a86a-bfcd202d0c1d · outbound

This paper cites an unresolved cited work.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Unresolved cited work

Reference 92

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Observation dd44d1a2-b787-46b8-a510-135f083610d0 · outbound

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Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems Web-scale k-means clustering

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:31.252352Z

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Observation 0bfe84f7-47db-4d13-96dd-c40fa377ce83 · outbound

This paper cites J.; Trucks, G.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems J.; Trucks, G

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Resolution: unresolved; no resolver link, observed 2026-08-01T06:49:31.349761Z

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Observation fdbe6c50-048c-4889-9d0a-387d109a034f · outbound

This paper cites H.; Teukolsky, S.

Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems H.; Teukolsky, S

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