Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review

Abstract

Comprehensive experimental investigation and accurate predictive models are required to understand the dynamics in Ionic liquid (IL) properties. Examples of these predictive models are empirical correlations, Quantitative Structure Property Relationship (QSPR) and Machine Learning (ML). In this study, we reported the application of various ML models for predicting thermo-physical properties of ILs. Our study showed that these ML models could be categorized into conventional and hybrid models. These conventional models include Artificial Neural Networks (ANN), Least Square Support Vector Machine (LSSVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Meanwhile, the hybrid models consist of random forest, gradient boosting, and group method of data handling. We provided an overview of these ML models and optimization methods such as genetic algorithm, particle swarm algorithm, and Coupled Simulated Annealing (CSA) algorithm, and their applications in IL studies. We observed that ANN, LSSVM, and ANFIS represent the three most frequently used ML approaches in predicting the various properties of ILs among the models discussed. The investigation revealed that the ANN approach is most widely used, while the studies involving the solubility of gases (H2S and CO2) represent the most common problems related to ML application in IL studies. However, the combination of conventional ML and optimization algorithms such as LSSVM-CSA gives better accuracy compared to ANN in most applications. It is noteworthy that system parameters (temperature and pressure) and critical properties (critical temperature and critical pressure) are the key thermo-physical that depicts the phase behavior of any ILs. Finally, to generalize MLs methods to certain ILs based on similarity in cations and anions, it is important to represent the molecular descriptions of the liquid as one of the property predictors.