Machine Learning-Assisted Survey on Charge Storage of MXenes in Aqueous Electrolytes

Abstract

Pseudocapacitance is capable of both high power and energy densities owing to its fast chemical adsorption with substantial charge transfer. 2D transition-metal carbides/nitrides (MXenes) are an emerging class of pseudocapacitive electrode materials. However, the factors that dominate the physical and chemical properties of MXenes are intercorrelated with each other, giving rise to challenges in the quantitative assessment of their discriminating importance. In this perspective, literature data on the specific capacitance of MXene electrodes in aqueous electrolytes is comprehensively surveyed and analyzed using machine-learning techniques. The specific capacitance of MXene electrodes shows strong dependency on their interlayer spacing, where confined H2O in the interlayer space should play a key role in the charge storage mechanism. The electrochemical behavior of MXene electrodes is overviewed based on atomistic insights obtained from data-driven approaches.