Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

Abstract

Metal-ion batteries (MIBs), including alkali metal-ion (Li+, Na+, and K+), multi-valent metal-ion (Zn2+, Mg2+, and Al3+), metal-air, and metal-sulfur batteries, play an indispensable role in electrochemical energy storage. However, the performance of MIBs is significantly influenced by numerous variables, resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement. Machine learning (ML), with its capability to solve intricate tasks and perform robust data processing, is now catalyzing a revolutionary transformation in the development of MIB materials and devices. In this review, we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years. We present an extensive overview of existing algorithms, elucidating their details, advantages, and limitations in various applications, which encompass electrode screening, material property prediction, electrolyte formulation design, electrode material characterization, manufacturing parameter optimization, and real-time battery status monitoring. Finally, we propose potential solutions and future directions for the application of ML in advancing MIB development.