Computational design of nanostructured materials for battery applications

Abstract

During the last two decades, nanostructured material has made impressive advances in the development of battery technology. The parallel and complimentary growth of experimental and computational approaches has made the development and deployment of battery material much faster and more reliable in terms of scientific understanding and technological implication. This chapter provides a detailed description of several widely used computational approaches from material to device level needed for the development of battery technology (specifically for the lithium-ion battery). Following are the few key properties of battery systems (including nanostructured materials) that can be calculated and evaluated with simulation and modeling techniques and can make batteries a fertile ground for computational materials design: (1) electrode properties (cell voltage, ionic and electronic conductivities, theoretical capacity predictions, phase stability, structure stability, thermal stability), (2) electrolyte properties (electrochemical stability, electrolyte oxidation reaction, electrolyte reduction reaction, screening of salt and additive), (3) separator (Li+ ion transport, interaction of anion and cation), and () catalyst (energy diagram, volcano plot, catalytic conversion, reaction mechanism, etc.). This chapter mainly highlights the computational approach based on physical theories (e.g., density functional theory, molecular dynamics, kinetic Monte Carlo, multiscale modeling) to model most of those above-mentioned properties while the data-driven (artificial intelligence/machine learning) methods are discussed in another chapter. � 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.