Machine Learning Approaches for Designing Electrode Materials for Lithium‐Ion Batteries

Lithium-ion batteries (LIBs) have enabled the widespread adoption of electric vehicles (EVs) and portable electronic devices and are growing in popularity. Increasingly, emphasis has been placed on sustainable and clean energy for efficient energy storage systems, leading to their accelerated adaptation. The electrodes of LIBs are a topic of great interest to improve the energy density, performance, and reliability of these batteries for wide-spread applications. Artificial intelligence (AI) and machine learning (ML) have proven to be extremely valuable tools for the development of computational models and optimization techniques in multiple fields of science. The developments of ML in predicting different characteristics of batteries, including electrolyte and electrode materials, and their interactions are hot topics in both industry and academia. This chapter highlights state-of-the-art techniques and achievements in applying ML to different aspects of electrode materials for improving various performance metrics of LIBs. The coupling of ML tools with physics-inspired computational models can produce deep insight and significantly fast-track research protocols and product development through integrated computational materials engineering (ICME) framework. This chapter depicts how outstanding results guided by ML are a promising alternative for complex computational calculations as a first screening tool for discovering materials for different battery applications. ML-based interatomic potentials have also been briefly covered. They turn out to be extremely useful in exploring the properties of battery materials at the atomistic length scales while simultaneously providing accuracy and speed. This chapter presents a detailed account of how ML techniques have been successfully applied for identifying, screening, and designing the electrode materials as well as optimizing the manufacturing processes, enhancing the performance of LIBs owing to the electrodes, and finding applications in the characterization of batteries for second life or chemical recycling. Some possible future directions for integration with onboard BMS and cloud BMS, IoT sensors, and blockchain have also been discussed. A roadmap for assessing material consumption through ML has also been provided for the realization of a sustainable future.