Machine learning assisted materials design and discovery for rechargeable batteries

Abstract

Abstract Machine learning plays an important role in accelerating the discovery and design process for novel electrochemical energy storage materials. This review aims to provide the state-of-the-art and prospects of machine learning for the design of rechargeable battery materials. After illustrating the key concepts of machine learning and basic procedures for applying machine learning in rechargeable battery materials science, we focus on how to obtain the most important features from the specific physical, chemical and/or other properties of material by using wrapper feature selection method, embedded feature selection method, and the combination of these two methods. And then, the applications of machine learning in rechargeable battery materials design and discovery are reviewed, including the property prediction for liquid electrolytes, solid electrolytes, electrode materials, and the discovery of novel rechargeable battery materials through component prediction and structure prediction. More importantly, we discuss the key challenges related to machine learning in rechargeable battery materials science, including the contradiction between high dimension and small sample, the conflict between the complexity and accuracy of machine learning models, and the inconsistency between learning results and domain expert knowledge. In response to these challenges, we propose possible countermeasures and forecast potential directions of future research. This review is expected to shed light on machine learning in rechargeable battery materials design and property optimization.