Multiscale computations and artificial intelligent models of electrochemical performance in Li‐ion battery materials

Abstract Li‐ion battery (LIB) is widely used as one of renewable energy resources for powerful electronics and electric vehicles. The main challenge in developing next‐generation LIB is to further improve the energy density, rate capability, and cycling stability of electrode and electrolyte materials. With the rapid development of computational science, the material design has changed from the traditional trial‐and‐error approach to integrated database‐based computation. Multiscale computational methods and machine learning (ML) not only speed up the development of new materials, but also provide insight into the intrinsic relationship between the microscopic composition and macroscopic performance of materials. In this review, we revealed the correlation of electrochemical activity of LIB materials with the response of energy to variable parameters such as charge‐transfer number and Li‐ion diffusion coordinates. Based on the structure–property relationship, we reviewed multiscale calculation and ML methods for electrochemical properties in LIB materials. A comparison for various applied methods was outlined to provide a method‐selecting reference in LIB material design. It is expected that a deep combination of multiscale computations, experimental data, ML should be more powerful methods for discovering new LIB materials. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Reaction Mechanisms and Catalysis