Energy storage is required in scale to enable the transition from the current carbon-based to a renewable-energy-driven economy. Hence shifting towards a paradigm wherein greater reliance on earth-abundant materials for developing energy storage technologies is essential. Ceria is among the rare earth oxides that are well distributed, and it exhibits good redox behavior (Ce4+/Ce3+) [1]. However, ceria also suffers from a bottleneck. It provides only moderate specific capacitance; hence given its relatively abundant global distribution, it deserves attention, especially regarding improved performance in supercapacitor applications [2-4]. Hence this focuses on three major themes with ceria as the material of focus: (i) capacitive enhancement, (ii) computational performance prediction driven materials design, (iii) data-driven prediction of material performance.

Initially, we demonstrate two approaches to enhance the supercapacitive performance of ceria: (i) the role of doping and (ii) the use of redox-additives in the traditional alkaline aqueous electrolyte. A dopant (with primary oxidation states as 4+ and 3+) is found to aid in the enhancement of (a) the oxygen-vacancy concentration and (b) lattice oxygen mobility, compared to pristine ceria [5]. Hence, doping standalone is shown to improve specific capacitance over pristine ceria significantly [6].

After that, we demonstrate redox-additive-based [K3Fe(CN)6] enhancement of charge-storage capacity. However, despite the enhancement reported by us (also consistent with recent reports from other groups), from a technology-development standpoint, we show that there are challenges associated with a lack of standard measurement techniques to evaluate the redox-additive-based device parameters [7]. To address this concern, we have developed a new method that enables the delineation of the contribution of the effective mass of the redox-additive on the electrode surface. This method is expected to be generic and will be used to evaluate other redox additive-based supercapacitor devices [8].

A Mott-Littleton-based method is utilized to choose the more suitable dopant; the design is then validated in a practical device [9-10]. After that, a machine learning-based paradigm is used to predict the material’s (cerium oxynitride) specific capacitance and cyclic stability. Significant outcomes of our proof-of-concept ‘prediction validation’ work on ceria-based supercapacitors are, (I) a single predictor – namely the oxygen vacancy formation energy - as elucidated by the Mott-Littleton method is found to be a good measure of suitability, and (II) data-driven approach is proven to be handy in predicting the performance of a novel material for supercapacitor application. The thesis edged towards a conclusion through an engineering validation of II.