Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications

MXene-based materials are characterized by excellent superconductivity, superb ion-holding capacity, large surface area, and rapid electrochemical reactions, making them viable options for applications in high-capacity energy storage and conversion systems (ESCS) such as portable digital devices, electric vehicles, power transportation, modern intelligent networks, and 5 G telecommunications. This review article looks at the latest developments and some of the difficulties in the synthesis and modification of MXene-based materials and highlights the transformative role of machine learning (ML) in advancing MXene research and applications. Applications in energy storage and water purification are discussed alongside the economic and industrial challenges of large-scale production. Recent studies confirm that ML models have been instrumental in improving MXene synthesis processes, enabling higher yields and optimization of properties, better purity, and scalability through real-time process control and reinforcement learning. Techniques such as genetic algorithms, evolutionary algorithms, and Bayesian optimization accelerate the discovery of novel MXene phases tailored for specific uses. The review identifies future directions in MXene research, emphasizing the development of scalable fabrication methods, ML-driven material informatics platforms, and the expansion of MXene applications in electronics and beyond. By integrating ML, MXene research is poised to achieve faster, cost-effective advancements and commercialization for next-generation technologies.