A Review of Computational Chemistry for Predicting Electrochemical Properties of Electrodes in Lithium Ion Batteries: Methods, Applications, and Challenges

Abstrak

Lithium-ion batteries (LIBs) are a key technology supporting the transition toward sustainable energy systems, particularly for electric vehicles, portable electronics, and large-scale energy storage. However, their development still faces major challenges related to electrochemical performance, safety, and the sustainability of electrode materials. Addressing these issues requires a fundamental understanding of electrode electrochemical properties at the atomic scale. In this context, computational chemistry has emerged as an effective approach to predict and interpret material behavior prior to experimental synthesis and testing. This review discusses the role of computational chemistry in predicting the electrochemical properties of lithium-ion battery electrode materials, with emphasis on applied methods, applications to cathode and anode materials, and current challenges. The review is based on a systematic analysis of recent literature involving Density Functional Theory (DFT), molecular dynamics simulations, and machine learning approaches. The results indicate that computational chemistry plays an important role in predicting working voltage, lithium-ion diffusion, structural stability, theoretical capacity, and the effects of defects and structural modifications on electrode performance. Nevertheless, limitations in accurately representing realistic battery operating conditions remain. Therefore, the integration of ab initio simulations, molecular dynamics, and machine learning is considered a promising strategy for improving predictive accuracy and efficiency in developing next-generation lithium-ion battery electrode materials.