Iravani, SiavashKhosravi, ArezooZare, Ehsan NazarzadehVarma, Rajender S.Zarrabi, AliMakvandi, Pooyan2024-12-152024-12-15202402046-206910.1039/d4ra06384h2-s2.0-85210100173https://doi.org/10.1039/d4ra06384hhttps://hdl.handle.net/20.500.14517/7508Varma, Rajender/0000-0001-9731-6228; Zarrabi, Ali/0000-0003-0391-1769; Iravani, Siavash/0000-0003-3985-7928; Nazarzadeh Zare, Ehsan/0000-0002-0446-4385This review explores the synergistic relationship between MXenes and artificial intelligence (AI), highlighting recent advancements in predicting and optimizing the properties, synthesis routes, and diverse applications of MXenes and their composites. MXenes possess fascinating characteristics that position them as promising candidates for a variety of technological applications, including energy storage, sensors/detectors, actuators, catalysis, and neuromorphic systems. The integration of AI methodologies provides a robust toolkit to tackle the complexities inherent in MXene research, facilitating property predictions and innovative applications. We discuss the challenges associated with the predictive capabilities for novel properties of MXenes and emphasize the necessity for sophisticated AI models to unravel the intricate relationships between structural features and material behaviors. Moreover, we examine the optimization of synthesis routes for MXenes through AI-driven approaches, underscoring the potential for streamlining and enhancing synthesis processes via data-driven insights. Furthermore, the role of AI is elucidated in enabling targeted applications of MXenes across multiple domains, illustrating the correlations between MXene properties and application performance. The synergistic integration of MXenes and AI marks the dawn of a new era in material design and innovation, with profound implications for advancing diverse technological frontiers.eninfo:eu-repo/semantics/closedAccess[No Keyword Available]ReviewQ2Q114493683536851WOS:00135959970000139574930