Machine learning in advancing anode materials for Lithium-Ion batteries – A review

Abstract

Lithium-ion batteries have become integral to the energy storage industry, driving innovations like electric vehicles, renewable energy systems, and portable electronics. A critical aspect of enhancing LIB performance lies in developing anode materials, which directly influence the batteries’ energy density, life cycle, and safety. In recent years, Machine Learning has emerged as a powerful tool in predicting, designing, and optimizing anode materials. This review explores the integration of ML techniques in advancing anode materials, including data-driven approaches to predicting electrochemical performance, optimizing synthesis processes, and discovering novel materials. Key ML methods such as supervised learning, unsupervised learning, and reinforcement learning are discussed in the context of improving material properties like capacity, conductivity, and stability. The paper also highlights current challenges, including the need for larger datasets, improved interpretability of ML models, and integrationof ML with experimental methods. The insights gained from this review provide a roadmap for future research on leveraging ML in developing next-generation anode materials for LIBs.