Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning | Paper Citation Record

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This paper cites Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction , journal =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction , journal =

Reference 1

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.700088Z

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Observation 0a037308-6aa1-45ff-899a-00c81cc36b4c · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 2

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Observation 6b7907c0-010d-46a9-837f-3a5d8ef63696 · outbound

This paper cites Prediction of Henry.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of Henry

Reference 3

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.619721Z

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Observation 57ed4fee-1ed7-43bd-ad7e-3adad14b4047 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 4

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.533828Z

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Observation 06a586f7-a88d-4f91-b258-bf06884c338f · outbound

This paper cites Hybridizing physical and data-driven prediction methods for physicochemical properties , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Hybridizing physical and data-driven prediction methods for physicochemical properties , volume =

Reference 5

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.441778Z

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Observation d243ca11-1893-4726-b634-fa3f0f6bf55d · outbound

This paper cites Perspective: Machine learning of thermophysical properties , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Perspective: Machine learning of thermophysical properties , volume =

Reference 6

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Observation 05b9a2d0-6243-466b-a614-0df94c721071 · outbound

This paper cites Blei and Alp Kucukelbir and Jon D.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Blei and Alp Kucukelbir and Jon D

Reference 7

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Observation b09cc329-d015-44a3-88f2-1acb53c03d4a · outbound

This paper cites Predicting activity coefficients at infinite dilution for varying temperatures by matrix completion , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Predicting activity coefficients at infinite dilution for varying temperatures by matrix completion , volume =

Reference 8

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.358767Z

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Observation cff6d5c4-ed33-4730-89d5-0fac8a803139 · outbound

This paper cites Prediction of temperature-dependent Henry’s law constants by matrix completion , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of temperature-dependent Henry’s law constants by matrix completion , volume =

Reference 9

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.265427Z

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Observation b61ab39a-d1ec-4d0c-8192-b898ebeaa794 · outbound

This paper cites Blei , title =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Blei , title =

Reference 10

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Observation 11902bd4-fec5-4376-868a-35db244e04c1 · outbound

This paper cites Hierarchical matrix completion for the prediction of properties of binary mixtures , ISSN =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Hierarchical matrix completion for the prediction of properties of binary mixtures , ISSN =

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:55.180848Z

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Observation 4f4d715f-cead-4a1c-9d90-7cf75b23e700 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:55.312046Z

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Observation 1ccd49e5-8339-40a2-b737-856193396829 · outbound

This paper cites Prediction of parameters of group contribution models of mixtures by matrix completion , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of parameters of group contribution models of mixtures by matrix completion , volume =

Reference 13

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.179407Z

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Observation f2cbc208-9a17-44a9-a315-aee54e617566 · outbound

This paper cites Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0 , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0 , volume =

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:55.582774Z

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Observation 6e46137f-9e52-4c3c-bce0-4f35fe079975 · outbound

This paper cites Prediction of pair interactions in mixtures by matrix completion , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of pair interactions in mixtures by matrix completion , volume =

Reference 15

Resolution: verified exact; doi, observed 2026-08-01T13:29:33.029339Z

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Observation e327e06a-ad43-4e0c-b8df-e174e26e4ce8 · outbound

This paper cites MLPROP – An Interactive Web Interface for Thermophysical Property Prediction with Machine Learning , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning MLPROP – An Interactive Web Interface for Thermophysical Property Prediction with Machine Learning , volume =

Reference 16

Resolution: verified exact; doi, observed 2026-08-01T13:29:32.809289Z

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Observation 393eaf78-d78d-42ec-992f-49093006fac9 · outbound

This paper cites Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures , volume =

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:55.884429Z

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Observation 80c84114-c26e-4aa5-9c57-756f21b7b026 · outbound

This paper cites Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions , volume =

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:55.971641Z

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Observation dcd34ad2-7fab-4428-9c08-e64d3a4d5057 · outbound

This paper cites Modified UNIFAC 2.0-A Group-Contribution Method Completed with Machine Learning , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Modified UNIFAC 2.0-A Group-Contribution Method Completed with Machine Learning , volume =

Reference 19

Resolution: verified exact; doi, observed 2026-08-01T13:29:32.595859Z

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Observation 738be70e-fd65-401f-8562-a76345f628ef · outbound

This paper cites Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors , volume =

Reference 20

Resolution: verified exact; doi, observed 2026-08-01T13:29:32.259668Z

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Observation dee487de-bc70-499c-aa9e-5f4f2ffc7376 · outbound

This paper cites , year =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning , year =

Reference 21

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.978031Z

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Observation 31e01ab1-28d0-47fe-a36c-c6bccbc98abe · outbound

This paper cites an unresolved cited work.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 22

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.876178Z

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Observation f4b53d7c-0d3c-4d2f-8df7-36f096c143dc · outbound

This paper cites and Sinclair, Donald A.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Sinclair, Donald A

Reference 23

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.694182Z

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Observation 61b7ae41-c745-4a76-9111-b1479562b634 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 24

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.482248Z

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Observation 17a7f030-26e9-42c0-9683-d149e0caaf0d · outbound

This paper cites Thermodynamik der Mischungen , ISBN =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Thermodynamik der Mischungen , ISBN =

Reference 25

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.290528Z

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Observation a1a8c146-304c-41d1-8390-029061bffc09 · outbound

This paper cites Predicting temperature‐dependent activity coefficients at infinite dilution using tensor completion , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Predicting temperature‐dependent activity coefficients at infinite dilution using tensor completion , volume =

Reference 26

Resolution: verified exact; doi, observed 2026-08-01T13:29:31.082039Z

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Observation 23b58978-406f-45c1-b6bc-fea88e636658 · outbound

This paper cites Improvement of Diffusion Coefficient Prediction by Active Learning , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Improvement of Diffusion Coefficient Prediction by Active Learning , volume =

Reference 27

Resolution: verified exact; doi, observed 2026-08-01T13:29:30.704025Z

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Observation cbfb621d-2dfc-4bba-8af8-aa494c70e5bb · outbound

This paper cites Prediction of Diffusion Coefficients in Mixtures with Tensor Completion.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of Diffusion Coefficients in Mixtures with Tensor Completion

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-01T13:29:30.388399Z

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Observation d457cae2-a567-4e62-963a-677dd2914210 · outbound

This paper cites Balancing molecular information and empirical data in the prediction of physico-chemical properties , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Balancing molecular information and empirical data in the prediction of physico-chemical properties , volume =

Reference 29

Resolution: verified exact; doi, observed 2026-08-01T13:29:29.990380Z

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Observation 8edd270c-3bc6-4bac-bf5e-5f717ec5e2b9 · outbound

This paper cites and Singh, Daljit and Ray, Parthasarathi and Sridhar, Srinivasan and Read, Stanley M.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Singh, Daljit and Ray, Parthasarathi and Sridhar, Srinivasan and Read, Stanley M

Reference 30

Resolution: verified exact; doi, observed 2026-08-01T13:29:29.627333Z

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Observation bef5ac81-aa3d-4e53-8a92-48ac93fed909 · outbound

This paper cites , year =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning , year =

Reference 31

Resolution: verified exact; doi, observed 2026-08-01T13:29:29.195762Z

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Observation 9ea8642c-e83b-4603-81c8-0e06a9a004fd · outbound

This paper cites A simple empirical model describing the thermodynamics of hydration of ions of widely varying charges, sizes, and shapes , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A simple empirical model describing the thermodynamics of hydration of ions of widely varying charges, sizes, and shapes , volume =

Reference 32

Resolution: verified exact; doi, observed 2026-08-01T13:29:28.869520Z

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Observation 81cf315c-0914-4f2a-b4c9-e52d62389420 · outbound

This paper cites and Hughes, Kevin J.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Hughes, Kevin J

Reference 33

Resolution: verified exact; doi, observed 2026-08-01T13:29:28.539839Z

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Observation 19c779a9-f0a6-4186-8625-b726ac669a26 · outbound

This paper cites and Mayorga, Guillermo , year =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Mayorga, Guillermo , year =

Reference 34

Resolution: verified exact; doi, observed 2026-08-01T13:29:28.201272Z

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Observation 9ff935d3-d62c-4454-827c-7bc5e71abb93 · outbound

This paper cites A revised LIQUAC and LIFAC model (LIQUAC/LIFAC) for the prediction of properties of electrolyte containing solutions , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A revised LIQUAC and LIFAC model (LIQUAC*/LIFAC*) for the prediction of properties of electrolyte containing solutions , volume =

Reference 35

Resolution: verified exact; doi, observed 2026-08-01T13:29:27.869392Z

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Observation 3cb8c3b1-e363-470a-9e71-321c566aee92 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Modified LIQUAC and Modified LIFACA Further Development of Electrolyte Models for the Reliable Prediction of Phase Equilibria with Strong Electrolytes , volume =

Reference 36

Resolution: verified exact; doi, observed 2026-08-01T13:29:27.450719Z

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Observation 6b4d77e3-d599-446f-9dbe-2cebe4b67ac5 · outbound

This paper cites A gE model for single and mixed solvent electrolyte systems , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A gE model for single and mixed solvent electrolyte systems , volume =

Reference 37

Resolution: verified exact; doi, observed 2026-08-01T13:29:27.068963Z

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Observation 8865310b-300c-43e8-aee2-e079738eb953 · outbound

This paper cites and Talbot, Nicola L.C.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Talbot, Nicola L.C

Reference 38

Resolution: verified exact; doi, observed 2026-08-01T13:29:26.766187Z

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Observation 9c0eb70e-7ccf-438a-904e-e8a76f171085 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Rard, Joseph A

Reference 39

Resolution: verified exact; doi, observed 2026-08-01T13:29:26.537109Z

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Observation b3bdadd5-d055-4b4c-bb85-decb1698aa34 · outbound

This paper cites Electrolyte Solutions: Thermodynamics, Crystallization, Separation methods.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Electrolyte Solutions: Thermodynamics, Crystallization, Separation methods

Reference 40

Resolution: verified exact; doi, observed 2026-08-01T13:29:26.162190Z

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Observation c5634941-d45a-4248-8689-49917dc38dd6 · outbound

This paper cites and Silvester, Leonard F.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Silvester, Leonard F

Reference 41

Resolution: verified exact; doi, observed 2026-08-01T13:29:25.675203Z

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Observation aa6dbe97-b7e9-4cda-82a5-7aa1a06d2caf · outbound

This paper cites an unresolved cited work.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 42

Resolution: verified exact; doi, observed 2026-08-01T13:29:25.408451Z

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Observation 5a6cbf9c-2876-4f90-8fcd-fc66118e502f · outbound

This paper cites Prediction of the density of aqueous electrolyte solutions with matrix completion methods , journal =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of the density of aqueous electrolyte solutions with matrix completion methods , journal =

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:57.645861Z

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Observation c49c4644-fad5-4613-afc9-1eb65ab80c32 · outbound

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning and Lazarou, G

Reference 44

Resolution: verified exact; doi, observed 2026-08-01T13:29:25.202705Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dc32d120-8832-4ba7-a423-ef78611f37d4 · outbound

This paper cites Open circuit voltage of an all-vanadium redox flow battery as a function of the state of charge obtained from UV-Vis spectroscopy , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Open circuit voltage of an all-vanadium redox flow battery as a function of the state of charge obtained from UV-Vis spectroscopy , volume =

Reference 45

Resolution: verified exact; doi, observed 2026-08-01T13:29:24.976387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7abf7add-4154-40db-89c9-f05e531400b6 · outbound

This paper cites Application of the Pitzer model for describing the evaporation of seawater , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Application of the Pitzer model for describing the evaporation of seawater , volume =

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:57.838934Z

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Observation a3dd29a1-294e-4d18-b095-4a18dce3163c · outbound

This paper cites GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures , volume =

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:57.911576Z

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This paper cites HANNA: hard-constraint neural network for consistent activity coefficient prediction , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning HANNA: hard-constraint neural network for consistent activity coefficient prediction , volume =

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:57.967866Z

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Observation eb9d9c64-c44b-4a88-9f8e-ab964647def7 · outbound

This paper cites Thermodynamically consistent machine learning model for excess Gibbs energy , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Thermodynamically consistent machine learning model for excess Gibbs energy , volume =

Reference 49

Resolution: verified exact; doi, observed 2026-08-01T13:29:24.834066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ed15b714-660f-4a55-b9ff-03205ae6d03d · outbound

This paper cites Artificial intelligence in thermodynamics: hybrid modeling of thermophysical properties of fluids , volume =.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Artificial intelligence in thermodynamics: hybrid modeling of thermophysical properties of fluids , volume =

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:58.122971Z

Source-reported events for the cited work

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Observation 014ed9a7-6c59-4246-bb32-2298b9a695ae · outbound

This paper cites Open circuit voltage of an all-vanadium redox flow battery as a function of the state of charge obtained from UV-Vis spectroscopy.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Open circuit voltage of an all-vanadium redox flow battery as a function of the state of charge obtained from UV-Vis spectroscopy

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:58.190622Z

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Unavailable: canonical work link unavailable.

Observation f94644eb-a9c4-43b0-8875-ce0c8640ccf3 · outbound

This paper cites Application of the Pitzer model for describing the evaporation of seawater.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Application of the Pitzer model for describing the evaporation of seawater

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:58.273391Z

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Observation 9ef95956-e267-4ad4-9993-4da55f1c6ceb · outbound

This paper cites an unresolved cited work.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:58.337968Z

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Observation 2e45c73c-07cb-4a8e-bafa-adca25d2d403 · outbound

This paper cites A.; Sinclair, D.

Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A.; Sinclair, D

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T13:26:58.394695Z

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Thermodynamik der Mischungen; Springer Berlin Heidelberg, 2017

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning S.; Mayorga, G

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Unresolved cited work

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A gE model for single and mixed solvent electrolyte systems

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Modified LIQUAC and Modified LIFACA Further Development of Electrolyte Models for the Reliable Prediction of Phase Equilibria with Strong Electrolytes

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A revised LIQUAC and LIFAC model (LIQUAC*/LIFAC*) for the prediction of properties of electrolyte containing solutions

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning C.; Hughes, K

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Perspective: Machine learning of thermophysical properties

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning MLPROP – An Interactive Web Interface for Thermophysical Property Prediction with Machine Learning

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning HANNA: hard-constraint neural network for consistent activity coefficient prediction

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Thermodynamically consistent machine learning model for excess Gibbs energy

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Artificial intelligence in thermodynamics: hybrid modeling of thermophysical properties of fluids

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of Diffusion Coefficients in Mixtures with Tensor Completion

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Hybridizing physical and data-driven prediction methods for physicochemical properties

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Predicting activity coefficients at infinite dilution for varying temperatures by matrix completion

Reference 75

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Hierarchical matrix completion for the prediction of properties of binary mixtures

Reference 76

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors

Reference 77

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Balancing molecular information and empirical data in the prediction of physico-chemical properties

Reference 78

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Predicting temperature‐dependent activity coefficients at infinite dilution using tensor completion

Reference 79

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of Henry s law constants by matrix completion

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of temperature-dependent Henry’s law constants by matrix completion

Reference 81

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction

Reference 82

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Improvement of Diffusion Coefficient Prediction by Active Learning

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of the density of aqueous electrolyte solutions with matrix completion methods

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions

Reference 85

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of pair interactions in mixtures by matrix completion

Reference 87

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning Prediction of parameters of group contribution models of mixtures by matrix completion

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning DDBST - Dortmund Data Bank Software And Separation Technology GmbH, Dortmund Data Bank, 2026

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning A simple empirical model describing the thermodynamics of hydration of ions of widely varying charges, sizes, and shapes

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning https://mc-stan.org

Reference 91

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning M.; Kucukelbir, A.; McAuliffe, J

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning C.; Talbot, N

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning L.; Rard, J

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Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning S.; Silvester, L

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