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Vanadium Diselenide (VSe2) is a prominent candidate in the 2D transition metal dichalcogenides (TMDs) family for energy storage applications. Herein, we report the experimental and theoretical investigations on the effect of cobalt doping in 1T-VSe2. The energy storage performance in terms of specific capacitance, stability and energy and power density is studied. It is observed that 3% Co doped VSe2 exhibits better energy storage performance as compared to other concentrations, with a specific capacitance of ~ 193 F/g in a two-electrode symmetric configuration. First-principles Density Functional Theory (DFT) based simulations support the experimental findings by suggesting an enhanced quantum capacitance value after the Co doping in the 1T-VSe2. By making use of the advantages of the specific electrode materials, a solid state asymmetric supercapacitor (SASC) is also assembled with MoS2 as the negative electrode. The assembled Co-VSe2//MoS2 SASC device shows excellent energy storage performance with a maximum energy density of 33.36 Wh/kg and a maximum power density of 5148 W/kg with a cyclic stability of 90% after 5000 galvanostatic charge discharge cycles.

Solar energy offers a promising means of addressing energy supply and storage problems, but this potential is not fully realized due to a lack of suitable semiconducting materials. The discovery of new materials with desirable properties has historically been conducted either using an experimental or a first‐principles density functional theory based study. These approaches are extremely time‐intensive, and therefore, cannot be applied effectively to study a large number of systems. In such situations, machine learning can be used to make predictions about properties of new compounds from known data, providing a more efficient route to materials discovery. Here, this approach is used to predict the bandgap of a series of oxysulfide perovskites (of the form of ABOXS3−X, X = 0,1,2,3), in general, and sulfur‐rich ABOS2, in particular. Atomic properties of constituent elements in the perovskite structures via 1.048 millions possible subsets of features are employed to train the models. Further, feature selection, kernel ridge regression, and k‐nearest neighbors classification methods are applied to downselect the promising ABOS2 based oxysulfide perovskites for water‐splitting. The accuracy of each model is determined using standard statistical metrics. Finally, seven stable but yet unsynthesized sulfur‐rich oxysulfide perovskites (BiInOS2, BiGaOS2, SbInOS2, SbGaOS2, SbAlOS2, SnZrOS2, and MgSnOS2) that show potential for water‐splitting applications are proposed.