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