Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/11363
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dc.contributor.authorDEY, NAYANen_US
dc.contributor.authorVENKATNATHAN, ARUNen_US
dc.date.accessioned2026-07-20T09:49:42Z
dc.date.available2026-07-20T09:49:42Z
dc.date.issued2026-07en_US
dc.identifier.citationAnnual Review of Materials Research, 56, 155-178.en_US
dc.identifier.issn1531-7331en_US
dc.identifier.issn1545-4118en_US
dc.identifier.urihttps://doi.org/10.1146/annurev-matsci-072924-113817en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/11363
dc.description.abstractSoft electrolytes, which combine the mechanical stability of solids and the higher ionic mobility of liquids, are promising materials for electrochemical devices. This review presents several theoretical models and computational methods for the examination of ion conduction, especially in the context of soft electrolytes. The difference in theoretical models for the accurate determination of transport properties depends on the choice of the electrolyte. Calculations using density-functional theory provide insight into the activation energy barriers associated with ion migration and interaction energies. Molecular dynamics (MD) and ab initio MD simulations are used for the determination of diffusion coefficients, ionic conductivity, and transference numbers, which can support experimental observations. Coarse-grained MD simulations provide insight on microstructure and dynamics. Machine learning algorithms trained from data derived using ab initio quantum chemistry calculations can be employed to rapidly screen a large number of materials, which can accelerate the synthesis of efficient electrolytes.en_US
dc.language.isoenen_US
dc.publisherAnnual Reviewen_US
dc.subjectIonic conductivityen_US
dc.subjectDensity-functional theoryen_US
dc.subjectMolecular dynamicsen_US
dc.subjectCoarse-graineden_US
dc.subjectMachine learningen_US
dc.subject2026-JUL-WEEK2en_US
dc.subjectTOC-JUL-2026en_US
dc.subject2026en_US
dc.titleModeling Ion Conduction in Soft Electrolytesen_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Chemistryen_US
dc.identifier.sourcetitleAnnual Review of Materials Researchen_US
dc.publication.originofpublisherForeignen_US
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