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 learning approaches can achieve orders of magnitude speedup over traditional DFT methods while maintaining chemical accuracy. We examine present computational constraints, particularly the difficulties in precisely simulating intricate dynamic processes and van der Waals interactions within MXene systems. The amalgamation of density functional theory (DFT) with machine learning methodologies presents a promising strategy for assessing several prospective 2D MXene configurations while preserving high predictive precision. Through the analysis of recent advancements in MXene-based systems, including vanadium carbide (V 2 CT x ) achieving experimental capacities up to 631 mAh g −1 , double transition metal VNbCT x reaching 520.5 mAh g −1 , andniobium carbide (Nb 2 CT x ) demonstrating versatile multi-ion storage capabilities across lithium, sodium, and potassium systems, we illustrate how integrated ML–DFT frameworks can expedite systematic discovery initiatives for dependable and efficient materials that are crucial for actualizing the potential of 2D MXene van der Waals heterostructures in advanced energy storage applications. Graphical abstract