A comprehensive review on MoSe nanostructures with an overview of machine learning techniques for supercapacitor applications.

In the past few decades, supercapacitors (SCs) have emerged as good and reliable energy storage devices due to their impressive power density, better charge-discharge rates, and high cycling stability. The main components of a supercapacitor are its electrode design and composition. Many compositions are tested for electrode preparations, which can provide good performance. Still, research is widely progressing in developing optimum high-performance electrodes. Metal chalcogenides have recently gained a lot of interest for application in supercapacitors due to their intriguing physical and chemical properties, unique crystal structures, tuneable interlayer spacings, broad oxidation states, MoSe, belonging to the family of Transition Metal Dichalcogenides (TMDs), has also been well explored recently for application in supercapacitors due to its similar properties to 2D materials. In this review, we briefly discuss supercapacitors and their classification. Various available synthesis routes for MoSe preparation are summarized. A detailed assessment of the electrochemical performances of different MoSe composites, including cyclic voltammetry (CV) analysis and galvanostatic charge-discharge (GCD) analysis, is given for symmetric and asymmetric supercapacitors. The limitations of MoSe and its composites are mentioned briefly. The use of machine learning methods and algorithms for supercapacitor applications is discussed for forecasting valuable details. Finally, a summary is provided, along with conclusions.