(356d) Development of an Artificial Neural Network Model to Predict Electrical Conductivity of Pure Ionic Liquids and Ionic Liquid-Ionic Liquid Mixtures
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Topical Conference: Applications of Data Science to Molecules and Materials
Applications of Data Science in Molecular Sciences II
Tuesday, November 17, 2020 - 8:45am to 9:00am
In this presentation, we will describe our efforts at developing a machine learning model based on the ionic conductivity data obtained from the NIST ILThermo Database. A feed forward artificial neural network (FFAAN) comprised of an input layer, a hidden layer and an output layer is obtained from the data on ~100 ionic liquids. We will demonstrate that the FFAAN is able to capture the data with a high degree of accuracy over five orders of magnitude. The machine learning model is then used to predict the ionic conductivity data for ~1200 pure ionic liquids obtained by combining the unique cations and anions in the database. We will also show how the machine learning model can be employed to predict the ionic conductivity for ~840,000 ionic liquids. The validity of such predictions will be demonstrated by comparing the ionic conductivity of those binary ionic liquids for which experimental data is available. Furthermore, we will report on 5000 potential binary ionic liquid-ionic liquid mixtures for which there exists a possibility for obtaining ionic conductivity higher than those of the pure ionic liquids making up the binary ionic liquid mixtures.