Prediction of ionic conductivity of imidazolium-based ionic liquids at different temperatures using multiple linear regression and support vector machine algorithms
Zi Kang Koi, Wan Zaireen Nisa Yahya, Kiki Adi Kurnia
Abstract
The conductivity of various imidazolium-based ILs has been predicted via QSPR approach using MLR and SVM regression coupled with stepwise model-building. This will aid the screening of suitable ILs with desired conductivity for specific applications.
Topics & Concepts
Support vector machineChemistryQuantitative structure–activity relationshipIonic liquidConductivityLinear regressionStepwise regressionRegression analysisRegressionBiological systemIonic conductivityMachine learningIonic bondingArtificial intelligenceAlgorithmStatisticsPhysical chemistryOrganic chemistryComputer scienceStereochemistryMathematicsIonCatalysisElectrolyteElectrodeBiologyIonic liquids properties and applicationsAdvanced Chemical Sensor TechnologiesElectrochemical Analysis and Applications