Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts | Paper Citation Record

Observation 943160f6-81f3-4bd7-9dc9-8d7f5b3c0b33 · outbound

This paper cites Molten salt for advanced energy applica- tions: A review.Annals of Nuclear Energy, 169:108924, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Molten salt for advanced energy applica- tions: A review.Annals of Nuclear Energy, 169:108924, 2022

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:26.663843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5f2864b-f2f8-40d7-8fd8-e994d962885b · outbound

This paper cites Evaluation of salt coolants for reactor applications.Nuclear technology, 163(3):330–343, 2008.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Evaluation of salt coolants for reactor applications.Nuclear technology, 163(3):330–343, 2008

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:26.718172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c13f8442-7e11-4018-be38-5552320bffc7 · outbound

This paper cites Thermophysical properties ofliquidchloridesfrom600to1600k: Meltpoint, enthalpy of fusion, and volumetric expansion.Journal of Molecular Liquids, 346:118147, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Thermophysical properties ofliquidchloridesfrom600to1600k: Meltpoint, enthalpy of fusion, and volumetric expansion.Journal of Molecular Liquids, 346:118147, 2022

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:26.763409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db80a8f5-0269-4f96-a0f3-ca51c5a81157 · outbound

This paper cites Review of commercial thermal energy storage in concentrated solar power plants: Steam vs.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Review of commercial thermal energy storage in concentrated solar power plants: Steam vs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:26.816927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13fb5ab3-9c7a-4a40-82f1-4cd3b5c24398 · outbound

This paper cites Operating temperature windows for fusion reactor structural materials.Fusion Engineering and design, 51:55–71, 2000.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Operating temperature windows for fusion reactor structural materials.Fusion Engineering and design, 51:55–71, 2000

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.001117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0530e103-c515-428e-bc12-358d88db2136 · outbound

This paper cites Development of robust neural-network interatomic potential for molten salt.Cell Reports Physical Science, 2(3), 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Development of robust neural-network interatomic potential for molten salt.Cell Reports Physical Science, 2(3), 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.058573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86a8c646-c537-4384-a38b-22238c43b911 · outbound

This paper cites The phase equilibrium dia- gram for the kcl-nacl system.Materials Research Bulletin, 2(10):935–938, 1967.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts The phase equilibrium dia- gram for the kcl-nacl system.Materials Research Bulletin, 2(10):935–938, 1967

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.133784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2392f41c-5f53-4e51-9a11-114b8af697fd · outbound

This paper cites Thermo- dynamics of the nacl–kcl system.Thermochimica acta, 606:25–33, 2015.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Thermo- dynamics of the nacl–kcl system.Thermochimica acta, 606:25–33, 2015

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.214775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f3ddf7-c433-48da-b017-1800c012cfd1 · outbound

This paper cites Molten salt storage for power generation.Chemie Inge- nieur Technik, 93(4):534–546, 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Molten salt storage for power generation.Chemie Inge- nieur Technik, 93(4):534–546, 2021

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.278201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6153e485-9df8-4f80-ae48-1187c52363d6 · outbound

This paper cites Physical properties data compilations relevant to energy storage.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Physical properties data compilations relevant to energy storage

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.326530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add7e269-8f98-4c1a-90ef-f85dde63cd9a · outbound

This paper cites Physical properties of molten-salt reactor fuel, coolant, and flush salts.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Physical properties of molten-salt reactor fuel, coolant, and flush salts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.401049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e8ba819-3d30-4c9c-a393-dc0ec4eff4b9 · outbound

This paper cites Experimental investigation and thermodynamic modeling of an innovative molten salt for thermal energy storage (tes).Applied energy, 212:516–526, 2018.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Experimental investigation and thermodynamic modeling of an innovative molten salt for thermal energy storage (tes).Applied energy, 212:516–526, 2018

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.469545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fc6dba4-048e-4154-9283-d1d1226522ac · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.583193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c24ae4d-4ca7-4123-af1c-169aefbdf176 · outbound

This paper cites Structure of molten nacl and the decay of the pair-correlations.The Journal of Chemical Physics, 157(9), 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Structure of molten nacl and the decay of the pair-correlations.The Journal of Chemical Physics, 157(9), 2022

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.606726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b3ee979-812f-4d6c-afad-04f402ec8a6d · outbound

This paper cites Microstructure and thermal properties of nacl–zncl2 molten salt by molecular dynam- ics simulation and experiment.Solar Energy Materials and Solar Cells, 250:112108, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Microstructure and thermal properties of nacl–zncl2 molten salt by molecular dynam- ics simulation and experiment.Solar Energy Materials and Solar Cells, 250:112108, 2023

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.627220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98cc3e19-cf9a-4960-930b-31501908a1b9 · outbound

This paper cites First-principles molecular dynamics modeling of the licl–kcl molten salt system.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts First-principles molecular dynamics modeling of the licl–kcl molten salt system

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.682548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00a073ee-6976-47ee-bf38-8de7de875337 · outbound

This paper cites Density-functional-based molecular-dynamics simulations of molten salts.The Journal of chemical physics, 123(13), 2005.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Density-functional-based molecular-dynamics simulations of molten salts.The Journal of chemical physics, 123(13), 2005

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.729959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9c36dd5-b5b9-4872-9d41-e6ac4936efd4 · outbound

This paper cites New advances in the study of local structure of molten binary salts.Physical review letters, 78(3):460, 1997.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts New advances in the study of local structure of molten binary salts.Physical review letters, 78(3):460, 1997

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.794034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e2fa0a4-ff66-4e96-8bf6-be4e2b320f39 · outbound

This paper cites Experimental techniques in molten fluoride chemistry.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Experimental techniques in molten fluoride chemistry

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.911101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f38e2e32-f9f0-410e-9587-8583dceeb503 · outbound

This paper cites Phase diagrams of binary and ternary fluoride systems.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Phase diagrams of binary and ternary fluoride systems

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:27.978213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5a69d8-8ee1-4d19-ab88-fd47cbfc236f · outbound

This paper cites Assessment of candidate molten salt coolants for the advanced high temperature reactor (ahtr).

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Assessment of candidate molten salt coolants for the advanced high temperature reactor (ahtr)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.033261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af2c0439-0b3e-49a3-b40c-5323c9c43f64 · outbound

This paper cites Corrosion in molten salts.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Corrosion in molten salts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.087170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37507410-8399-4a55-8cf9-3c5359837d09 · outbound

This paper cites Inhomogeneous elec- tron gas.Physical review, 136(3B):B864, 1964.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Inhomogeneous elec- tron gas.Physical review, 136(3B):B864, 1964

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.128801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95598173-6b8a-4247-86f9-1e1aad45293d · outbound

This paper cites The structure of molten flinak.Journal of Nuclear Materials, 537:152219, 2020.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts The structure of molten flinak.Journal of Nuclear Materials, 537:152219, 2020

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.185111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58b862b-c9cf-4a70-943c-4728935a1658 · outbound

This paper cites First-principles-derived transport properties of molten chloride salts.Journal of Nuclear Materials, 585:154601, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts First-principles-derived transport properties of molten chloride salts.Journal of Nuclear Materials, 585:154601, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.210806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ecf9b6-b4ff-4c58-9e13-f90f17f9ab0a · outbound

This paper cites Interionic potentials in alkali halides and their use in simulations of the molten salts.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Interionic potentials in alkali halides and their use in simulations of the molten salts

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.280088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa9a53a9-ee59-4e76-a3fc-0fa99a75cf3f · outbound

This paper cites Polarization effects in ionic systems from first principles.Journal of Physics: Con- densed Matter, 5(17):2687–2706, 1993.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Polarization effects in ionic systems from first principles.Journal of Physics: Con- densed Matter, 5(17):2687–2706, 1993

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.330005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acbb3a02-2708-4abc-bf8b-88a84d32ebe6 · outbound

This paper cites Reaxff reactive force field for molecular dynam- 14 ics simulations of hydrocarbon oxidation.The Journal of Physical Chemistry A, 112(5):1040–1053, 2008.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Reaxff reactive force field for molecular dynam- 14 ics simulations of hydrocarbon oxidation.The Journal of Physical Chemistry A, 112(5):1040–1053, 2008

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.396599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aed433b-931a-4c8f-ab0b-cc0a0006765d · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.441489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9e3cf75-763b-49b0-a87f-818ce531ae94 · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.490485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9988dee-ee23-4599-80ce-3b9dbcff47d3 · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.520374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e57d94a4-306a-4980-b30a-8cb15c052065 · outbound

This paper cites Reaxffmgh reactive force field for magnesium hydride systems.The Journal of Physical Chemistry A, 109(5):851–859, 2005.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Reaxffmgh reactive force field for magnesium hydride systems.The Journal of Physical Chemistry A, 109(5):851–859, 2005

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.606939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 090f9e84-7156-4aba-9fb2-3aea720834fa · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.666565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d98148e-9258-4a25-9f3e-0f777890c49a · outbound

This paper cites Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.Iscience, 27(5), 2024.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.Iscience, 27(5), 2024

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.709736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b40a7257-b778-4e02-ba7b-a62835b57441 · outbound

This paper cites Computing chemical potentials with machine- learning-acceleratedsimulationstoaccuratelypredictther- modynamic properties of molten salts.Chemical Science, 16(7):3078–3091, 2025.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Computing chemical potentials with machine- learning-acceleratedsimulationstoaccuratelypredictther- modynamic properties of molten salts.Chemical Science, 16(7):3078–3091, 2025

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.767588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 215dc4d1-dab5-43cb-ae61-6a7c43f29d4d · outbound

This paper cites Development of deep potentials of molten mgcl2–nacl and mgcl2–kcl salts driven by machine learning.ACS Applied Materials & Interfaces, 15(11):14184–14195, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Development of deep potentials of molten mgcl2–nacl and mgcl2–kcl salts driven by machine learning.ACS Applied Materials & Interfaces, 15(11):14184–14195, 2023

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.797208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07793cdf-9fad-4eba-b819-4b34652d79b5 · outbound

This paper cites Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials.Journal of Materials Science & Technology, 75:78–85, 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials.Journal of Materials Science & Technology, 75:78–85, 2021

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.831191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90c7c34a-0782-4cab-83fb-08fdcb5e2be3 · outbound

This paper cites Molecular dynamics simulations of molten magnesium chloride using machine-learning-based deep potential.Advanced Theory and Simulations, 3(12):2000180, 2020.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Molecular dynamics simulations of molten magnesium chloride using machine-learning-based deep potential.Advanced Theory and Simulations, 3(12):2000180, 2020

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.919214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde2354d-92f6-43fe-ab0b-f9bbb5742cb7 · outbound

This paper cites Mol- ecular dynamics simulation of molten strontium chloride based on deep potential.Journal of Molecular Liquids, 348:118380, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Mol- ecular dynamics simulation of molten strontium chloride based on deep potential.Journal of Molecular Liquids, 348:118380, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.943312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff5c271-7965-49d7-9e79-997512349a75 · outbound

This paper cites A dft accurate machine learning description of molten zncl2 and its mixtures: 1.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts A dft accurate machine learning description of molten zncl2 and its mixtures: 1

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:28.994122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae9e80f1-791d-45d0-b76a-1f927d4451e5 · outbound

This paper cites Machine learning accelerates molten salt simulations: Thermal conductivity of mgcl2-nacl eutectic.Advanced Theory and Simulations, 5(8):2200206, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Machine learning accelerates molten salt simulations: Thermal conductivity of mgcl2-nacl eutectic.Advanced Theory and Simulations, 5(8):2200206, 2022

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.046934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e71e792-7587-42e7-976d-cb10c15fee5b · outbound

This paper cites Machine- learning-driven simulations on microstructure and ther- mophysical properties of mgcl2–kcl eutectic.ACS applied materials & interfaces, 13(3):4034–4042, 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Machine- learning-driven simulations on microstructure and ther- mophysical properties of mgcl2–kcl eutectic.ACS applied materials & interfaces, 13(3):4034–4042, 2021

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.136146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df413bfc-86da-4950-95a8-7d7adcea3965 · outbound

This paper cites Theoretical prediction on the redox potentials of rare-earth ions by deep potentials.Ionics, 27(5):2079–2088, 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Theoretical prediction on the redox potentials of rare-earth ions by deep potentials.Ionics, 27(5):2079–2088, 2021

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.188268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20920db4-0ea6-4e35-bcd1-4c4be33381f6 · outbound

This paper cites A dft accurate machine learning description of molten zncl2 and its mixtures: 2.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts A dft accurate machine learning description of molten zncl2 and its mixtures: 2

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.236483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a81d742d-577e-46ab-b9f9-376d78c111ad · outbound

This paper cites Transferable deep learning potential reveals intermediate- range ordering effects in lif–naf–zrf4 molten salt.Jacs Au, 2(12):2693–2702, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Transferable deep learning potential reveals intermediate- range ordering effects in lif–naf–zrf4 molten salt.Jacs Au, 2(12):2693–2702, 2022

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.289592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 167e7045-7f85-41b5-a904-5f9803533994 · outbound

This paper cites Moment tensor potentials: A class of systematically improvable interatomic potentials.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Moment tensor potentials: A class of systematically improvable interatomic potentials

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.370627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9788dd23-a0b3-4c13-8736-3dd7f8122ceb · outbound

This paper cites Interatomic potential for sodium and chlorine in both neutral and ionic states.Physical Review B, 109(17):174113, 2024.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Interatomic potential for sodium and chlorine in both neutral and ionic states.Physical Review B, 109(17):174113, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.424973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5867e0e-c79f-4095-8714-dbd388d4e3b4 · outbound

This paper cites Dft accurate interatomic potential for molten nacl from machine learning.The Journal of Physical Chemistry C, 124(47):25760–25768, 2020.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Dft accurate interatomic potential for molten nacl from machine learning.The Journal of Physical Chemistry C, 124(47):25760–25768, 2020

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.469718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a13332ff-533b-432d-8fed-c54763e158c6 · outbound

This paper cites Solubility of sodium in sodium chloride: a density func- tional theory molecular dynamics study.Journal of The Electrochemical Society, 161(8):E3042–E3048, 2014.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Solubility of sodium in sodium chloride: a density func- tional theory molecular dynamics study.Journal of The Electrochemical Society, 161(8):E3042–E3048, 2014

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.516133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27a2dff6-8300-4715-81b9-5da12a2fd6ed · outbound

This paper cites New benchmark set of transition-metal coordination reactions for the assessment of density func- tionals.Journal of chemical theory and computation, 10(8):3092–3103, 2014.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts New benchmark set of transition-metal coordination reactions for the assessment of density func- tionals.Journal of chemical theory and computation, 10(8):3092–3103, 2014

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.570792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb76f83e-281d-4aa6-955c-3321fdb50a64 · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.627341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c579c72-4017-4401-9361-7983471ab5bf · outbound

This paper cites Accelerating training of mlips through small-cell training.Journal of Materials Research, 38(24):5095–5105, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Accelerating training of mlips through small-cell training.Journal of Materials Research, 38(24):5095–5105, 2023

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.672024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27f135f5-f75e-4fb1-8f99-e46e0112327f · outbound

This paper cites A set of moment tensor potentials for zirconium with increasing complexity.Journal of Chemical Theory and Computation, 19(19):6848–6856, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts A set of moment tensor potentials for zirconium with increasing complexity.Journal of Chemical Theory and Computation, 19(19):6848–6856, 2023

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.730913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77c8c4c8-7a94-40c5-a663-592de9ddf24f · outbound

This paper cites Small-cell-based fast active learning of machine learning interatomic potentials.Computational Materials Science, 256:113919, 2025.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Small-cell-based fast active learning of machine learning interatomic potentials.Computational Materials Science, 256:113919, 2025

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.775665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f7a59da-3c6d-4748-9f04-31fd6e363e5d · outbound

This paper cites The mlip 15 package: moment tensor potentials with mpi and ac- tive learning.Machine Learning: Science and Technology, 2(2):025002, 2021.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts The mlip 15 package: moment tensor potentials with mpi and ac- tive learning.Machine Learning: Science and Technology, 2(2):025002, 2021

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.839446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7299364e-524a-4746-97d3-0d26cf86a8ba · outbound

This paper cites Zuo and et al.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Zuo and et al

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.883485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16e56a6e-eb4f-4205-88f6-80b7d1cb1261 · outbound

This paper cites Quantum espresso: a modular and open-source software project for quantum simulations of materials.Journal of physics: Condensed matter, 21(39):395502, 2009.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Quantum espresso: a modular and open-source software project for quantum simulations of materials.Journal of physics: Condensed matter, 21(39):395502, 2009

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.918729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ace1af3-405b-4b61-bc20-3c183ece91a2 · outbound

This paper cites Advanced capabilities for materials modelling with quantum espresso.Journal of physics: Condensed matter, 29(46):465901, 2017.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Advanced capabilities for materials modelling with quantum espresso.Journal of physics: Condensed matter, 29(46):465901, 2017

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:29.989918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f7fbc26-db9b-46e2-8b78-5281c92fd62c · outbound

This paper cites From ultra- soft pseudopotentials to the projector augmented-wave method.Physical review b, 59(3):1758, 1999.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts From ultra- soft pseudopotentials to the projector augmented-wave method.Physical review b, 59(3):1758, 1999

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.022404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88fab9c3-680c-42bd-93b9-617c785e3954 · outbound

This paper cites Generalized gradient approximation made simple.Physi- cal review letters, 77(18):3865, 1996.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Generalized gradient approximation made simple.Physi- cal review letters, 77(18):3865, 1996

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.080281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 719fdd69-c8a5-442f-b694-ab695d4ff123 · outbound

This paper cites Assess- ment of the perdew–burke–ernzerhof exchange-correlation functional.The Journal of chemical physics, 110(11):5029– 5036, 1999.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Assess- ment of the perdew–burke–ernzerhof exchange-correlation functional.The Journal of chemical physics, 110(11):5029– 5036, 1999

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.123004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f41e56f7-9952-4d6b-8a39-e19d7505eb64 · outbound

This paper cites Special points for brillouin-zone integrations.Physical review B, 13(12):5188, 1976.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Special points for brillouin-zone integrations.Physical review B, 13(12):5188, 1976

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.158416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 064da92a-dc45-4a05-8a85-5a811709bef3 · outbound

This paper cites Lammps-a flexible simula- tion tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer physics communications, 271:108171, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Lammps-a flexible simula- tion tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer physics communications, 271:108171, 2022

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.170440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b3b8ff9-2c27-4da1-8d5e-4ef200797e05 · outbound

This paper cites The nose-hoover thermostat.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts The nose-hoover thermostat

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.233370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab5f6cde-7906-471f-bd6b-abaa142e3977 · outbound

This paper cites Polymorphic transitions in single crystals: A new molecular dynamics method.Journal of Applied physics, 52(12):7182–7190, 1981.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Polymorphic transitions in single crystals: A new molecular dynamics method.Journal of Applied physics, 52(12):7182–7190, 1981

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.278108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75b2c70c-8ce3-456b-ad19-4d47aa1dfb8d · outbound

This paper cites Active learning of linearly parametrized interatomic potentials.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Active learning of linearly parametrized interatomic potentials

Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.339741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1581e682-5bd5-487e-b1f1-f4f65271e9bd · outbound

This paper cites Mlip-3: Active learning on atomic environments with moment tensor potentials.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Mlip-3: Active learning on atomic environments with moment tensor potentials

Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.371526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eec41b29-a00c-4fea-9ade-0a3b9463b8cb · outbound

This paper cites Accelerating crystal structure prediction by machine-learning inter- atomic potentials with active learning.Physical Review B, 99(6):064114, 2019.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Accelerating crystal structure prediction by machine-learning inter- atomic potentials with active learning.Physical Review B, 99(6):064114, 2019

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.410460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c98c7130-fd50-4409-b774-05d35a033e75 · outbound

This paper cites Ephemeral data derived potentials for random structure search.Physical Review B, 106(1):014102, 2022.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Ephemeral data derived potentials for random structure search.Physical Review B, 106(1):014102, 2022

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.486675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 578f26b2-51de-4255-a9ea-dba26d248599 · outbound

This paper cites Crystal structure prediction using ab initio evolutionary techniques: Princi- ples and applications.The Journal of chemical physics, 124(24), 2006.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Crystal structure prediction using ab initio evolutionary techniques: Princi- ples and applications.The Journal of chemical physics, 124(24), 2006

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.515000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51a52b59-0d28-40b3-acdc-4ec9c9f5b84f · outbound

This paper cites How evolutionary crystal structure prediction works—and why.Accounts of chemical research, 44(3):227–237, 2011.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts How evolutionary crystal structure prediction works—and why.Accounts of chemical research, 44(3):227–237, 2011

Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.530987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a0a63c8-722c-4b5a-bfd7-0c7ce8d75fee · outbound

This paper cites New developments in evolutionary struc- ture prediction algorithm uspex.Computer Physics Com- munications, 184(4):1172–1182, 2013.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts New developments in evolutionary struc- ture prediction algorithm uspex.Computer Physics Com- munications, 184(4):1172–1182, 2013

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.597105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 559c502c-f65e-40f9-b681-1be98be6b111 · outbound

This paper cites Randspg: an open-source program for generating atomistic crystal structures with specific spacegroups.Computer Physics Communications, 213:208–216, 2017.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Randspg: an open-source program for generating atomistic crystal structures with specific spacegroups.Computer Physics Communications, 213:208–216, 2017

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.653904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ef9efd-478a-4605-92f6-724bbfbc2a99 · outbound

This paper cites A consistent and accurate ab initio parametriza- tion of density functional dispersion correction (dft-d) for the 94 elements h-pu.The Journal of chemical physics, 132(15), 2010.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts A consistent and accurate ab initio parametriza- tion of density functional dispersion correction (dft-d) for the 94 elements h-pu.The Journal of chemical physics, 132(15), 2010

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.687404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a618f3dd-616d-4af4-9980-1efe57cc20f1 · outbound

This paper cites Effect of the damping function in dispersion corrected density functional theory.Journal of computational chemistry, 32(7):1456–1465, 2011.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Effect of the damping function in dispersion corrected density functional theory.Journal of computational chemistry, 32(7):1456–1465, 2011

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.746501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66734971-e57c-4d77-8155-24bdb2bf263a · outbound

This paper cites Atomicclusterexpansion for quantum-accurate large-scale simulations of carbon.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Atomicclusterexpansion for quantum-accurate large-scale simulations of carbon

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.785406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4671f342-ee2c-4d75-b74e-a2fb76608d85 · outbound

This paper cites A comparison of methods for melting point calculation using molecular dynamics simulations.The Journal of chemical physics, 136(14), 2012.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts A comparison of methods for melting point calculation using molecular dynamics simulations.The Journal of chemical physics, 136(14), 2012

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.861228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5dbb2aa-3813-416a-a6e2-85ad56a6439f · outbound

This paper cites Nonequilibrium free-energy calculation of solids using lammps.Computational Materials Science, 112:333–341, 2016.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Nonequilibrium free-energy calculation of solids using lammps.Computational Materials Science, 112:333–341, 2016

Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.923044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b934b346-a757-4497-aca0-1b13e47b2216 · outbound

This paper cites Vegard’s law.Phys- ical review A, 43(6):3161, 1991.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Vegard’s law.Phys- ical review A, 43(6):3161, 1991

Reference 79

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:30.976107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1e0028c-f3d7-4dcc-8c66-35a6567286b2 · outbound

This paper cites Structural properties of nacl and kcl under pressure.Journal of Physics C: Solid State Physics, 19(15):2623–2632, 1986.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Structural properties of nacl and kcl under pressure.Journal of Physics C: Solid State Physics, 19(15):2623–2632, 1986

Reference 80

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.041163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35c3ce9b-95a6-49a6-83fa-c09c30d130c7 · outbound

This paper cites Studies of nacl-kcl solid solutions.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Studies of nacl-kcl solid solutions

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.152473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6b67e40-f39d-4e8a-85d2-c4e21538ef90 · outbound

This paper cites an unresolved cited work.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.227989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b688595e-193e-495f-87f4-4f211da27a05 · outbound

This paper cites The density of liq- uid sodium–potassium eutectic.High temperature, 41(3):340–345, 2003.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts The density of liq- uid sodium–potassium eutectic.High temperature, 41(3):340–345, 2003

Reference 83

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.280398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae120481-51f5-4dd7-ba99-fb578d532b71 · outbound

This paper cites Experi- mental determination of the thermal diffusivity of molten alkali halides by the forced rayleigh scattering method.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Experi- mental determination of the thermal diffusivity of molten alkali halides by the forced rayleigh scattering method

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.343927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cac0d44-6f31-4310-ac0c-3d866625f66b · outbound

This paper cites Thermodynamic investigation of the nacl- kcl salt system from 25 to 950°c.Journal of Molecular Liquids, 391:122591, 2023.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Thermodynamic investigation of the nacl- kcl salt system from 25 to 950°c.Journal of Molecular Liquids, 391:122591, 2023

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.448696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21174da9-3a25-4456-a0d2-bbb2742d4886 · outbound

This paper cites Thermal conductivity in molten alkali halides: composition dependence in mixtures of (na–k) cl.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Thermal conductivity in molten alkali halides: composition dependence in mixtures of (na–k) cl

Reference 86

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.512666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ed41118-c43f-492a-852b-eb3ab6e2505c · outbound

This paper cites Nist-janaf thermochemical tables.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Nist-janaf thermochemical tables

Reference 87

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.559455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09eb7903-5065-4523-af3a-ba0bf3016ae5 · outbound

This paper cites Heat capacities and thermal co- efficients of sodium’s and eutectic sodium–potassium’s coolants for nuclear reactors.Applied Sciences, 15(13):7566, 2025.

Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts Heat capacities and thermal co- efficients of sodium’s and eutectic sodium–potassium’s coolants for nuclear reactors.Applied Sciences, 15(13):7566, 2025

Reference 88

Resolution: unresolved; no resolver link, observed 2026-07-31T03:21:31.635936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers