Recent advances in the high entropy materials for advanced energy storage with machine learning

High-entropy materials (HEMs) show exceptional mechanical properties, highly adjustable chemical characteristics, and outstanding stability, making them suitable for energy storage. However, the broad compositional space and intricate chemical interactions in HEMs present challenges to traditional trial-and-error research methods, restricting their efficacy in swift screening and synthesis. Hence, the application of machine learning (ML) to the realm of high-entropy materials and energy storage becomes imperative. ML demonstrates its formidable capabilities for navigating the complexity of HEMs, with their diverse metal components, structures and property combinations, to advance energy storage applications. This review comprises the following sections: a concise introduction to the general process of ML in the energy materials field, a summary of HEMs in the energy storage field, a review of the latest achievements of ML in the HEMs and energy storage field, and finally, an exploration of current challenges and prospects in this interdisciplinary arena. With the advent of ML, the precision of its predictions and the efficiency of its screening methods have offered novel perspectives for material research, expediting the discovery and application of new materials. This article contributes to the advancement of research in related fields, hastening the development of novel materials to meet the escalating energy demands and promote sustainable development goals.