Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

Abstract Lithium‐sulfur batteries (LSBs) have attracted increasing attention in the past decades due to their great potential to the next‐generation high‐energy‐density storage systems. As important as electrodes, electrolytes that could strongly determine battery performance via component regulation have left big difficulties in clarifying their complex interactions caused by multicomponent as well as the intricate formation mechanism of passivation layers at the electrolyte‐electrode interfaces. Fortunately, machine learning (ML), which is time‐saving and highly efficient, has played an irreplaceable role in accelerating discovery, design, and optimization of novel electrochemical energy storage materials. In this concept, we summarize the complex issues present in multicomponent electrolytes and focus on optimization of electrolyte formulations based on ML in order to provide an outlook on the recent development and perspectives on LSBs from the viewpoint of high‐performance electrolytes.