The transition to renewable energy sources and the electrification of transportation have created an unprecedented demand for advanced energy storage technologies. The accelerating demand for efficient energy storage solutions has driven unprecedented innovation in materials science, with computational design emerging as a transformative approach for discovering next-generation battery and supercapacitor materials. This comprehensive review examines the latest developments in computational methodologies for energy storage material design, including density functional theory, machine learning approaches, and high-throughput screening techniques. Computational materials science has emerged as a powerful paradigm for accelerating energy storage innovation by enabling rapid screening of material properties and prediction of optimal compositions before experimental synthesis. We discuss the fundamental principles underlying computational materials discovery, their implementation across diverse energy storage systems, and the challenges associated with property prediction, structural optimisation, and experimental validation. Key applications spanning lithium-ion batteries, solid-state electrolytes, sodium-ion systems, and supercapacitor materials are analysed through recent breakthrough studies. The integration of artificial intelligence with quantum mechanical calculations, automated synthesis pathways, and multi-scale modelling represents promising frontiers for accelerating energy storage innovation. This review highlights both current achievements and future opportunities in developing predictive, interpretable, and efficient computational frameworks for energy storage applications.
Ann Ogechi Felix (Sat,) studied this question.