Transition Metal Molybdates Emerging Materials for High-Performance Supercapacitors: A Machine Learning Analysis

Transition metal molybdates (AMoO₄ where A = Ni, Co, Mn, Fe, and Zn) have attracted much attention as promising electrode materials for energy storage devices due to their multi-electron redox capability, higher electrical conductivity, good chemical and thermal stability, and stable crystal structure to get superior electrochemical performance. Transition metal molybdates and their graphene-based composites possess multidimensional morphology for supercapacitors. The morphology-dependent supercapacitor behavior has been reviewed in the present article. The formation mechanism of AMoO₄ nanostructures in the form of 1D, 2D, and 3D has been identified and respective supercapacitor behavior is outlined. The density functional theory based on the calculated electronic properties of AMoO₄ has been discussed. Additionally, the application of machine learning techniques in predicting and analyzing the relationships of AMoO₄ has been discussed for the first time. By leveraging ML algorithms, we identify key parameters influencing their energy storage capabilities, providing insights into the rational design of molybdate-based composites. Integrating experimental results with ML-driven optimization offers a novel pathway for accelerating the development of next-generation energy storage devices. In conclusion, future perspectives and challenges have been discussed.