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

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, K x MnO 2 , have been widely investigated for KIB application due to their high energy and power density. However, K x MnO 2 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 K x MnO 2 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 K 0.3 Mn 0.9 Cu 0.1 O 2 (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. A new materials discovery platform based on combined machine learning (ML) and density functional theory (DFT) for screening and experimental validation is proposed for designing a stable K x MnO 2 cathode in K-ion batteries.