Machine learning-enhanced exploration of 2D MXene van der Waals heterostructures for energy storage

Abstract The development of materials for advanced battery technologies is being revolutionized by two-dimensional (2D) MXene van der Waals (vdW) heterostructures, which offer improved regulation of interfacial chemistry, ion mobility, and electronic structure. Despite their considerable theoretical promise for energy storage applications, MXenes encounter challenges related to material instability and the extensive array of unexamined heterostructure combinations that impede practical implementation. This perspective explores the potential of machine learning (ML) to transform the identification of resilient and efficient 2D MXene van der Waals heterostructures for electrochemical energy storage. Machine…