Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

Abstract

Ionic liquids (ILs) and deep eutectic solvents (DESs) as green solvents have attracted dramatic attention recently due to their highly tunable properties. However, traditional experimental screening methods are inefficient and resource-intensive. The article provides a comprehensive overview of various ML algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting trees (GBT), etc., which have demonstrated exceptional performance in handling complex and high-dimensional data. Furthermore, the integration of ML with quantum chemical calculations and conductor-like screening model-real solvent (COSMO-RS) has significantly enhanced predictive accuracy, enabling the rapid screening and design of novel solvents. Besides, recent ML applications in the prediction and design of ILs and DESs focused on solubility, melting point, electrical conductivity, and other physicochemical properties become more and more. This paper emphasizes the potential of ML in solvent design, overviewing an efficient approach to accelerate the development of sustainable and high-performance materials, providing guidance for their widespread application in a variety of industrial processes.