Developing Machine Learning Models for Ionic Conductivity of Imidazolium-Based Ionic Liquids

22 April 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In this work, we have developed machine learning models based on support vector machine (SVM) and artificial neural network (ANN) to correlate ionic conductivity of imidazolium-based ionic liquids. The data, collected from the NIST ILThermo Database, spans six orders of magnitude and ranges from 275-475 K. Both models were found to exhibit very good performance. The ANN-model was then used to predict ionic conductivity for all the possible combinations of cations and anions contained in the original dataset, which led to the identification of an ionic liquid with 30% higher ionic conductivity than the highest conductivity reported in the database at 298 K. The model was further employed to predict ionic conductivity of binary ionic liquid mixtures. A large number of ionic liquid mixtures were found to possess non-ideal behavior in that an intermediate mole fraction for such ionic liquid mixtures resulted in either a maximum or minimum in the ionic conductivity.


Ionic Liquids
Ionic Conductivity
Machine Learning
Artificial Neural Network
Binary Ionic Liquids

Supplementary materials

supporting information


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