Machine Learning Paradigm for Advanced Battery Electrolyte Development

ABSTRACT As a crucial component of rechargeable batteries, electrolytes significantly determine interfacial characteristics and device performance. The development of advanced electrolytes relied on empirical trial‐and‐error and theoretical calculations mainly. Recently, the data‐driven methods represented by machine learning (ML) have made progress in the materials field. However, interdisciplinary barriers and the multicomponent complexity of electrolyte systems impede the advancement of artificial intelligence (AI) in the electrolyte domain. Therefore, examining the application of ML methods in liquid and solid‐state electrolytes to bridge the gap between battery science and data science is necessary. Firstly, this review summarizes the main technical routes and ion transport mechanisms of different electrolytes. Secondly, the basic concepts and suitable tasks of various ML algorithms are outlined. Following this, we investigate mainstream representation methods for electrolyte materials, which transfer material structures to machine‐readable input vectors. Subsequently, representative application cases of ML in liquid and solid‐state electrolytes are summarized. Finally, a perspective on the current challenges and future frontiers of AI‐driven electrolyte research is provided. This review offers a deep understanding of this interdisciplinary field, providing intelligent insight for advanced battery electrolyte innovation.