Electron Density Learning of Z-Bonds in Ionic Liquids and Its Application.

Ionic liquids (ILs) exhibit fascinating properties due to special Z-bonds and have been widely used in electrochemical systems. The local Z-bond networks potentially cause a discrepancy in electrochemical properties. Understanding the correlations between the Z-bond energy () and the electrochemical properties is helpful to identify appropriate ILs. It is difficult to estimate the correlations from single density functional theory calculations or molecular dynamic simulations. In this work, a machine learning model targeting the electronic density () of Z-bonds has been trained successfully, as expected for use in systems above the nanoscale size. The connection between the and the electrochemical potential window in ILs@TiO, as well as that between the and the charge carrier mobility in ILs-PEDOT:Tos@SiO, was separately investigated. This study highlights an efficient model for predicting ρ in nanoscale systems and anticipates exploring the connection between Z-bonds and the electrochemical properties of IL-based systems.