Interface and surface engineering: The nexus of MXenes, MOFs, and AI in hybrid material design for energy storage/conversion

Hybrid materials with tunable properties, particularly metal–organic frameworks (MOFs) and MXene composites, have become a forefront research area in energy storage and conversion systems. The electrochemical performance of these hybrids is governed by several critical factors, including the intrinsic characteristics of MOFs, synthesis methods, structural morphology, and advanced interface engineering techniques such as chemical modification, hybridization, and surface doping. These strategies significantly enhance conductivity, stability, ion transport, and charge transfer efficiency, making MOF@MXene composites highly effective for applications in supercapacitors, batteries, and energy conversion processes like hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Furthermore, artificial intelligence (AI) and machine learning (ML) techniques including deep learning, genetic algorithms, Bayesian optimization, support vector machines (SVM), random forest, and density functional theory (DFT)-assisted ML models play an important role in optimizing MXene and MOF interfaces by predicting ideal material combinations, refining synthesis methods, and guiding design. This nexus of MXenes, MOFs, and AI highlights the immense potential of MOF@MXene composites in shaping a sustainable energy future.