In-Silico Investigation of Promising Anode Materials for Li-Ion Batteries

Development of computational resources and methods have taken a lead in current times to reliably predict promising electrode materials for the rechargeable batteries in a cost and time effective manner. This may provide an efficient route to guide experimentalists in improving the battery performance and realizing the next-generation energy efficient batteries for grid storage and electrical vehicles in terms of capacity and energy density. In view of that, using various approaches like the first-principles density functional theory and evolutionary algorithms, we comprehensively study a variety of materials to develop an understanding at the atomistic level and predict efficient anode materials for the Li-ion batteries.