Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications

Abstract

This review addresses a critical need in the emerging intersection of digital chemistry and material properties by critically evaluating the current capabilities and future prospects of applying machine learning (ML) to ionic liquids (ILs) for thermal energy storage (TES) applications. We examine available data sets for key thermophysical properties relevant to TES, including thermal conductivity, heat capacity, density, viscosity, surface tension, melting point, enthalpy, CO2 solubility, and toxicity. Particular emphasis is placed on chemical space coverage, biases, and practical limitations. We compare different modeling approaches, spanning from conventional QSPR/QSAR and group-contribution methods to contemporary ML approaches, including kernel models, ensemble methods, graph neural networks (GNNs), and deep learning architectures. The impact of molecular representations and descriptor choices and validation strategies in determining model robustness and transferability is examined. To structure this assessment, we introduce a three-tier property-selection framework for IL-based TES and apply a six-criterion evaluation checklist consistently across all nine reviewed properties. The analysis is supported by literature-summary tables that consolidate data sets, chemical space coverage, modeling approaches, and validation practices across different property domains. Finally, we outline recommendations for data set standardization, open sharing of data and code, and the establishment of benchmark validation protocols to improve reproducibility and accelerate the discovery of ILs for TES applications.