Machine learning promotes the development of all-solid-state batteries

• Published in: Jiégòu huàxué Vol. 42; no. 9; pp. 100118 - 94
• Main Authors: Qiu, Yong, Zhang, Xu, Tian, Yun, Zhou, Zhen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023; Interdisciplinary Research Center for Sustainable Energy Science and Engineering(IRC4SE2),School of Chemical Engineering Zhengzhou University,Zhengzhou,450001,China
• Subjects: Big data; Machine learning; Performance prediction; Solid-state batteries; Performance prediction; Big data; Solid-state batteries; Machine learning
• ISSN: 0254-5861
• DOI: 10.1016/j.cjsc.2023.100118

Lithium-ion batteries (LIBs) are a promising energy storage system for green energy applications. However, the use of liquid electrolytes in LIBs results in safety and lifespan issues. To address these challenges, researchers have been focusing on the development of all-solid-state batteries that use solid electrolytes. Unfortunately, traditional methods are time-consuming and expensive for exploring solid-state batteries, limiting their ability to keep up with growing social demand. In recent years, the development of big data has opened up new avenues for materials discovery, allowing for large-scale materials screening through computer simulations and machine learning models that can disclose the structure-activity relationship of materials. This review provides an overview of the basic procedures and common algorithms used in machine learning for designing solid-state batteries, with particular emphasis on recent research progress in applying machine learning to cathode materials and solid electrolytes, as well as predicting the condition of solid-state batteries. Additionally, this review offers a brief outlook on the challenges and opportunities facing machine learning methods in the realm of solid-state batteries. [Display omitted] Rapid development of big data has opened up new avenues for material discovery to disclose the structure-activity relationship. The basic procedures and key features implemented in machine learning for designing solid-state batteries, including cathode materials and solid electrolytes, as well as predicting the condition of solid-state batteries are comprehensively covered.