Machine learning-inspired battery material innovation

Abstract

Machine learning (ML) techniques have been a powerful tool responsible for many new discoveries in materials science in recent years. In the field of energy storage materials, particularly battery materials, ML techniques have been widely utilized to predict and discover materials’ properties. In this review, we first discuss the key properties of the most common electrode and electrolyte materials. We then summarize recent progress in battery material advancement using ML techniques, through the three main strategies of direct property predictions, machine learning potentials, and inverse design. The major challenges, advantages and limitations of these techniques are also discussed. Finally, we conclude this review with a perspective on sustainable battery development using ML.