Machine Learning-Enabled Discovery of Ionic Liquid-Solvent Electrolytes Exhibiting High Ionic Conductivity

Abstract

Ionic Liquids (IL), which are a class of materials with versatile nature and growing popularity are facing impediments toward wide-spread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with another solvent. In a combinatorial sense, there exists an immense number of specific IL-solvent combinations. An exhaustive experimental or even simulation based-investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine-learning (ML) based models developed from existing databases. Although there exists prior literature that integrates machine learning to investigate mixtures of a specific solvent with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified ML model to predict ionic conductivity of any IL-solvent mixture system. In this regard, three models, namely, Random Forest, XGBoost, and Artificial Neural Network were formulated using a diverse dataset curated from the NIST ILThermo database. The dataset contained 549 unique ILs from 16 cation families, 81 unique solvents, representing a total of 23691 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models approximately 2.5 million IL-solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19,000 IL-solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.