A new material discovery platform of stable layered oxide cathodes for K-ion batteries

Abstract

The search for advanced electrode materials in K-ion batteries (KIBs) is a significant challenge due to the lack of an efficient throughput screening method in modern battery technology. Layered oxide cathode materials, KxMnO2, have been widely investigated for KIB application due to their high energy and power density. However, KxMnO2 suffers from structural instability and highly hygroscopic nature. To address these issues, here, a combined machine learning (ML) and first-principles method based on density functional theory (DFT) for screening and experimental validation is developed for the first time. This method is used for designing stable KxMnO2 that can reinforce the structural and environmental stabilities as well as high electrochemical performances. Among the large number of candidates, notably, the ML and DFT-assisted strategies identify P ' 3-type K0.3Mn0.9Cu0.1O2 (KMCO) as a promising candidate for a high performance KIB cathode. Finally, the experimental protocol proves that the KMCO cathode has substantially improved K-storage properties with high-power density and cycling stability even after four weeks air-exposure period. We believe that this study opens a new avenue for identifying and developing suitable electrode materials for future battery applications.