Leveraging machine learning for accelerated materials innovation in lithium-ion battery: a review

Abstract

As energy demands continue to rise in modern society, the development of high-performance lithium-ion batteries (LIBs) has become crucial. However, traditional research methods of material science face challenges such as lengthy timelines and complex processes. In recent years, the integration of machine learning (ML) in LIB materials, including electrolytes, solid-state electrolytes, and electrodes, has yielded remarkable achievements. This comprehensive review explores the latest applications of ML in predicting LIB material performance, covering the core principles and recent advancements in three key inverse material design strategies: high-throughput virtual screening, global optimization, and generative models. These strategies have played a pivotal role in fostering LIB material innovations. Meanwhile, the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.