Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions | Paper Citation Record

Observation 2d1b859e-a54d-425b-b4c4-35fa1b94db8e · outbound

This paper cites F.; Berendsen, H.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions F.; Berendsen, H

Reference 1

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Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 2

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Observation f929dec7-07b6-4803-a76d-70bb1d5bb82a · outbound

This paper cites D.; McCammon, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions D.; McCammon, J

Reference 3

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Observation 3ddb4245-db3a-4657-ac27-5b4b1b7dca79 · outbound

This paper cites CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields

Reference 4

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Observation e918dd38-927e-43b2-b178-e7be5a6a7a32 · outbound

This paper cites M.; Caldwell, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Caldwell, J

Reference 5

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This paper cites L.; Tirado-Rives, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Tirado-Rives, J

Reference 6

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Observation fe06103a-d6b4-4347-b381-88b41cbc4f00 · outbound

This paper cites E.; Van Gunsteren, W.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions E.; Van Gunsteren, W

Reference 7

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Observation 337852c9-905e-4ca9-b4db-1de5118b9fcd · outbound

This paper cites M.; Koner, D.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Koner, D.; Meuwly, M

Reference 8

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Observation a57a67f0-d1d8-4ece-91a2-e6f4df318a99 · outbound

This paper cites M.; Mondal, P.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Mondal, P.; Meuwly, M

Reference 9

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Observation 633ac706-60d0-4d83-9ef3-da0f34252a7c · outbound

This paper cites Permutationally invariant, reproducing kernel-based potential energy surfaces for polyatomic molecules: From formaldehyde to acetone.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Permutationally invariant, reproducing kernel-based potential energy surfaces for polyatomic molecules: From formaldehyde to acetone

Reference 10

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Observation de1976fc-159f-4ac1-88a4-d428f7f58d13 · outbound

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Reference 11

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Observation a80f1d53-b511-4372-9e03-4180ab456353 · outbound

This paper cites M.; Dawes, R.; Xie, D.; Guo, H.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Dawes, R.; Xie, D.; Guo, H

Reference 12

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Observation eea535df-92ca-4c27-88ce-6c3052963a3c · outbound

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Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 13

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Observation c658e3c9-d58d-49b7-9381-51d9ce9953fd · outbound

This paper cites The Theory of Intermolecular Forces; Oxford University Press: Cambridge, 2013.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions The Theory of Intermolecular Forces; Oxford University Press: Cambridge, 2013

Reference 14

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Observation 4d0a19af-484e-45e4-b10e-f7b828502bfb · outbound

This paper cites M.; Hawe, G.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Hawe, G

Reference 15

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Observation f39817f2-4b6f-4834-b1a5-d20eca73e509 · outbound

This paper cites Leveraging Symmetries of Static Atomic Multipole Electrostatics in Molecular Dynamics Simulations.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Leveraging Symmetries of Static Atomic Multipole Electrostatics in Molecular Dynamics Simulations

Reference 16

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Observation a64109ef-7eb6-4e99-99a5-ea65f4ce6c1e · outbound

This paper cites G.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions G.; Meuwly, M

Reference 17

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Observation e0a2587f-c71c-4278-b676-045c8498e00c · outbound

This paper cites Many-Body Effects and Electrostatics in Biomolecules; Jenny Stanford Publishing, 2016; pp 251--286.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Many-Body Effects and Electrostatics in Biomolecules; Jenny Stanford Publishing, 2016; pp 251--286

Reference 18

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Observation 5812a40d-99b9-4065-877a-1f5ca1ae1364 · outbound

This paper cites Y.; Qi, R.; Walker, B.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Y.; Qi, R.; Walker, B

Reference 19

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Observation 20625720-6d31-4968-bb37-77ee78236962 · outbound

This paper cites An efficient water force field calibrated against intermolecular THz and Raman spectra.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions An efficient water force field calibrated against intermolecular THz and Raman spectra

Reference 20

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Reference 21

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Observation 19c71d2d-53d9-4780-8934-fe6a359c8437 · outbound

This paper cites On Combination Rules for Molecular Van Der Waals Potential-well Parameters.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions On Combination Rules for Molecular Van Der Waals Potential-well Parameters

Reference 22

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Observation 737aedad-0715-4f31-8688-4c39897b7beb · outbound

This paper cites Inadequacy of the Lorentz-berthelot Combining Rules for Accurate Predictions of Equilibrium Properties by Molecular Simulation.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Inadequacy of the Lorentz-berthelot Combining Rules for Accurate Predictions of Equilibrium Properties by Molecular Simulation

Reference 23

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Observation 7bc118d7-a37f-45bd-b031-58170273ad29 · outbound

This paper cites A.; Pineda, L.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions A.; Pineda, L

Reference 24

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Observation 7a2b136d-f0ab-4ff4-a933-0b71e0f6e4ba · outbound

This paper cites The Q-AMOEBA (CF) Polarizable Potential.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions The Q-AMOEBA (CF) Polarizable Potential

Reference 25

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Observation 18703ec9-4b46-490a-ab8a-511c82268c72 · outbound

This paper cites P.; Kim, L.; Head-Gordon, M.; Head-Gordon, T.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions P.; Kim, L.; Head-Gordon, M.; Head-Gordon, T

Reference 26

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Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 27

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Observation 2889b485-f95d-4ffe-b161-86e4ba07678b · outbound

This paper cites T.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions T.; Meuwly, M

Reference 28

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Observation efe8f23f-1107-4bd4-82be-1d100b3fd4f0 · outbound

This paper cites T.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions T.; Meuwly, M

Reference 29

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Observation 9b6e74a8-e9f8-4371-92c9-2bbbe0b7f6b2 · outbound

This paper cites The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks

Reference 30

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Observation 237fef22-57a9-4f15-82d6-d04cf1170856 · outbound

This paper cites D.; Upadhyay, M.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions D.; Upadhyay, M.; Meuwly, M

Reference 31

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Observation c0afa6ea-9877-4534-bc30-4a92a822cc7f · outbound

This paper cites O.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions O.; Meuwly, M

Reference 32

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Observation 84553169-21eb-4c06-a3e8-fa49fb0b933f · outbound

This paper cites Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions

Reference 33

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Reference 34

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Observation 53840515-29c4-4fc9-9839-f5421626351e · outbound

This paper cites Kernel-Based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Kernel-Based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:16.217418Z

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Observation 1a6fbce9-427e-46f8-9b4b-ccc404742ac5 · outbound

This paper cites A.; Goedecker, S.; Behler, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions A.; Goedecker, S.; Behler, J

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:16.099027Z

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Observation ca9c0262-5058-4d4f-8ad0-6cccc6b9fc6f · outbound

This paper cites L.; Kaminski, G.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Kaminski, G

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.960041Z

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Observation 49bc49c8-6807-4d9f-945f-14ed30b4f7e7 · outbound

This paper cites D.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions D.; Meuwly, M

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.801125Z

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Observation 0f914be6-8d74-44f5-89c7-adb4059dd971 · outbound

This paper cites Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.624475Z

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Observation 47535b47-af43-456a-8eaa-9e5dea630cd7 · outbound

This paper cites Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:53:03.489498Z

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Observation 73b58d2e-9c89-4c15-a4c8-0a17b00bee7f · outbound

This paper cites L.; Conte, R.; Nandi, A.; Bowman, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Conte, R.; Nandi, A.; Bowman, J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.465631Z

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Observation d5aa43e7-97e3-4297-9fb8-b18c9f7e9e47 · outbound

This paper cites F.; Paesani, F.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions F.; Paesani, F

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.308271Z

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Observation fe7f4ba8-b403-4371-aa4e-1a52d7460bed · outbound

This paper cites L.; Chandrasekhar, J.; Madura, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Chandrasekhar, J.; Madura, J

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:15.169179Z

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Observation c0d35329-1385-4248-b211-e05600e4496d · outbound

This paper cites GFN2-xTB—An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions GFN2-xTB—An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions

Reference 44

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Observation 2e36f7bc-569f-49f5-b535-94ddd65f86e5 · outbound

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Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:14.988318Z

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Observation db0c5d73-99c4-4b5c-bfc3-ed18f3cd2859 · outbound

This paper cites B.; Knizia, G.; Werner, H.-J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions B.; Knizia, G.; Werner, H.-J

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:14.833056Z

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Observation 82225650-13f5-401d-aef5-c284337bbdf4 · outbound

This paper cites G.; Pearlmutter, B.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions G.; Pearlmutter, B

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:14.687382Z

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Observation ac6e9d8c-cb73-4aed-9a63-f7d053bc85fb · outbound

This paper cites On the Convergence of Adam and Beyond.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions On the Convergence of Adam and Beyond

Reference 48

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Observation 12fc3b9a-f302-46ab-b957-3903cc335916 · outbound

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Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:14.541463Z

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Observation e2b107b2-32c7-49fb-9394-8efa1493df4b · outbound

This paper cites L.; Blondel, A.; Boittier, E.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Blondel, A.; Boittier, E

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:14.373427Z

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Observation dee3f7f9-3e78-47a0-8339-ac8bbb674ce5 · outbound

This paper cites Constructing multidimensional molecular potential energy surfaces from ab initio data.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Constructing multidimensional molecular potential energy surfaces from ab initio data

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:14.208328Z

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Observation 9eb1b230-56f7-4df0-9467-da6bc9197a66 · outbound

This paper cites K.; Straight, S.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions K.; Straight, S

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:14.028522Z

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Observation af2b1c60-636e-4734-99f7-e72ac2d6a1a5 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 53

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Observation 0bd165d3-f2ad-4fff-ab58-569cdb4ab80c · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:13.643317Z

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Observation 16d203e0-1f19-474c-885e-c452cb2fbc60 · outbound

This paper cites o decker, M.; Schweer, S. M.; Dupont, J.; Lep \`e re, V.; Zehnacker-Rentien, A.; Suhm, M. A.; Schr \.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions o decker, M.; Schweer, S. M.; Dupont, J.; Lep \`e re, V.; Zehnacker-Rentien, A.; Suhm, M. A.; Schr \

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:13.482529Z

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Observation 540f6210-6a3b-4ad9-a9fe-af6f1634ee00 · outbound

This paper cites D.; Devereux, M.; Meuwly, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions D.; Devereux, M.; Meuwly, M

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:13.316112Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79bb578d-698c-48d7-b8e9-695c890f9557 · outbound

This paper cites B97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions B97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:13.119097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6222e7a7-0d79-4863-a77e-5bc541f76579 · outbound

This paper cites A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:12.878949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 162d4f55-be01-4ed5-bbfb-ee697ae92fcb · outbound

This paper cites A unified formulation of the constant temperature molecular dynamics methods.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions A unified formulation of the constant temperature molecular dynamics methods

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:12.720857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2eb30050-ff4f-4de3-beb3-edb49e2ff659 · outbound

This paper cites VMD -- V isual M olecular D ynamics.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions VMD -- V isual M olecular D ynamics

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:12.555155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 19cdd587-6230-450f-a282-dec1bde40a38 · outbound

This paper cites Characterization of the Local Structure in Liquid Water by Various Order Parameters.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Characterization of the Local Structure in Liquid Water by Various Order Parameters

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:12.333807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2e6524f0-df01-4c95-96c2-64cb8f2c6db3 · outbound

This paper cites L.; Faires, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Faires, J

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:12.118057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a81a116d-b859-446f-a303-10c110a914fd · outbound

This paper cites L.; Jenson, C.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Jenson, C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:11.926399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a066ff54-de73-4ffe-be7b-09dd9d25f614 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:11.718051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d3974e3f-f61c-4def-b023-1c0d2d3bc5e0 · outbound

This paper cites Dipole moment fluctuation formulas in computer simulations of polar systems.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Dipole moment fluctuation formulas in computer simulations of polar systems

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:11.539291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dfb0b077-ac31-42b0-83a7-36766592ce01 · outbound

This paper cites Molecular dynamics simulation of a polymer chain in solution.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Molecular dynamics simulation of a polymer chain in solution

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:11.382004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30badf80-1c6d-4c01-8c29-da53bf682d16 · outbound

This paper cites System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:11.138514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e4754a3-4d2d-4b29-8365-ed19c6b85889 · outbound

This paper cites K.; Meuwly, M.; Karplus, M.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions K.; Meuwly, M.; Karplus, M

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:10.942663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2ac429b7-6d2b-4557-b48f-93ce31855ed6 · outbound

This paper cites On the design space between molecular mechanics and machine learning force fields.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions On the design space between molecular mechanics and machine learning force fields

Reference 69

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T21:53:03.390272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 895269a2-3185-4bb3-9374-751b0be11384 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:10.777339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b7beb161-3e7b-4f69-8976-ea83bc387104 · outbound

This paper cites o pfer, K.; K \.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions o pfer, K.; K \

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:58.988361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24960f2f-f9ea-40dc-b3e5-7eb2d282a70a · outbound

This paper cites L.; Sewell, T.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Sewell, T

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:10.559348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ec63ca08-5cb8-4469-aa79-694920ffa990 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:10.366823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe20ac25-b1dc-488a-b7c2-9119f18b9451 · outbound

This paper cites E.; Rao, K.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions E.; Rao, K

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:10.131452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fce0f238-c253-40c6-9fe1-8dce264b7a08 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:09.911166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b2730554-c0bb-4ca6-ac70-16c43bacdefc · outbound

This paper cites E.; Lan, Z.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions E.; Lan, Z

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:09.724653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a9b8b61d-9c02-4cd8-89f6-ca0720ab8fb7 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:09.547359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21ff491c-b9ae-498d-8b8b-2d44499a139c · outbound

This paper cites J.; Brooks III, C.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions J.; Brooks III, C

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:09.379453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a742004b-1041-4656-804b-a5ada44fcf92 · outbound

This paper cites A.; Mead, R.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions A.; Mead, R

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:09.248654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0492b4a8-7a55-4dd3-a736-d8c09a3c1133 · outbound

This paper cites Ueber Projectionsmodelle der regelmässigen vier-dimensionalen Körper.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Ueber Projectionsmodelle der regelmässigen vier-dimensionalen Körper

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:09.122516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f0865d5-e3c4-4486-933d-4d30748a3697 · outbound

This paper cites Polarizable multipolar molecular dynamics using distributed point charges.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Polarizable multipolar molecular dynamics using distributed point charges

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:08.945393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 571010de-8420-4384-9db0-64b9d712653b · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:08.805694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 63abbd6e-3325-4c0a-bd0f-d34ff2a52e68 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:08.607714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1b2ad0c-f19b-4fe8-aae7-3700d7299e92 · outbound

This paper cites M.; Artacho, E.; Fernández-Serra, M.-V.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions M.; Artacho, E.; Fernández-Serra, M.-V

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:08.440260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b4600f1-c95e-4f2b-b65f-9b8e9425aba5 · outbound

This paper cites Systematic parametrization of polarizable force fields from quantum chemistry data.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Systematic parametrization of polarizable force fields from quantum chemistry data

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:08.313296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 142da9c7-ffe5-4ca5-be97-bd5d1102bb2a · outbound

This paper cites F.; Paesani, F.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions F.; Paesani, F

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:08.110872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9ac4a931-6c66-482b-923e-4e109e6518ee · outbound

This paper cites C.; Tschumper, G.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions C.; Tschumper, G

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:07.969649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a11cda4-d372-4418-9dfe-f652ad297056 · outbound

This paper cites Sublimation pressure and sublimation enthalpy of H _2 O ice Ih between 0 and 273.16 K.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Sublimation pressure and sublimation enthalpy of H _2 O ice Ih between 0 and 273.16 K

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:07.788035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation df8d23d4-f4e9-4330-8a5e-c2cbafc4a6ad · outbound

This paper cites Can We Learn the Energy of Sublimation of Ice from Water Clusters?.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Can We Learn the Energy of Sublimation of Ice from Water Clusters?

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:53:03.258131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37766207-e39d-4bb7-bdbd-333ede9e1e77 · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:07.621154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 78f234d6-0556-466a-a61d-ca05e6ad7c6f · outbound

This paper cites D.; Wang, L.-P.; Huggins, D.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions D.; Wang, L.-P.; Huggins, D

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:07.363100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2a16c43d-74b2-4d94-9ea3-e5999227f7a2 · outbound

This paper cites V.; Roux, B.; MacKerell Jr, A.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions V.; Roux, B.; MacKerell Jr, A

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:07.072888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a553b7bf-43fa-4f4f-a9a1-75da7235dc4d · outbound

This paper cites T.; Pettersson, L.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions T.; Pettersson, L

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:06.702780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f81b64d6-dda6-4f00-a322-59896f279fb7 · outbound

This paper cites L.; Conte, R.; Nandi, A.; Bowman, J.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions L.; Conte, R.; Nandi, A.; Bowman, J

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:06.416958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3096179a-60bc-40a0-be1b-d64b9d7fe9b4 · outbound

This paper cites R.; Debenedetti, P.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions R.; Debenedetti, P

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:06.088176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 58105c18-ce14-46f3-9295-4beb3dcea012 · outbound

This paper cites H.; Tsironi, I.; Mariedahl, D.; Blanco, M.; Huotari, S.; Honkimäki, V.; Nilsson, A.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions H.; Tsironi, I.; Mariedahl, D.; Blanco, M.; Huotari, S.; Honkimäki, V.; Nilsson, A

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:05.850662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91f456a7-ea6a-446d-a27c-a7c44bd52979 · outbound

This paper cites E.; Head-Gordon, T.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions E.; Head-Gordon, T

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:05.665401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71ec92ba-29f8-4e84-8df2-a264fd0c2a5c · outbound

This paper cites an unresolved cited work.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:53:05.452938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6ed18cea-d189-4a84-aef5-f0b62f510165 · outbound

This paper cites The Q-AMOEBA (CF) Polarizable Potential.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions The Q-AMOEBA (CF) Polarizable Potential

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:53:03.136928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4f563138-692a-4d62-ad65-a76609e1d90c · outbound

This paper cites Thermal conductivity, shear viscosity and specific heat of rigid water models.

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Thermal conductivity, shear viscosity and specific heat of rigid water models

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:53:05.201811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

Outbound references

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