Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning - Liu, Hongcan; Ma, Shun; Wu, Junjun; Wang, Yingkai; Wang, Xinghui (Frontiers in Energy Research, 2021)
Compared to liquid electrolytes, lithium solid-state electrolytes have received increased attention in the field of all-solid-state lithium ion batteries due to safety requirements and higher energy density. However, solid-state electrolytes face many challenges, including lower ionic conductivity, complex interfaces, and unstable physical or electrochemical properties. One of the most effective strategies is to find a new type of lithium solid-state electrolyte with improved properties. Traditional trial and error methods require resources and time to verify the new solid-state electrolytes. Recently, new lithium solid-state electrolytes were predicted through machine learning (ML), which has proved to be an efficient and reliable method for screening new functional materials. This paper reviews the lithium solid-state electrolytes that have been discovered based on ML algorithms. The selection and preprocessing of datasets in ML technology are initially discussed before describing the latest developments in screening lithium solid-state electrolytes through different ML algorithms in detail. Lastly, the stability of candidate solid-state electrolytes and the challenges of discovering new lithium solid-state electrolytes through ML are highlighted.