A Discovery Platform for Developing Stable Layered Oxide Cathodes for Potassium-Ion Batteries

The search for advanced electrode materials for potassium-ion batteries (KIBs) presents a significant challenge due to the absence of efficient high-throughput screening methods in modern battery technology. Layered oxide cathodes, such as KxMnO2, have been widely explored for KIB applications due to their high energy and power density. However, KxMnO2 faces challenges with structural instability and its highly hygroscopic nature. To tackle these issues, we introduce, for the first time, a combined machine learning (ML) and first-principles approach based on density functional theory (DFT) for screening and experimental validation. This method enables the design of stable KxMnO2 cathodes with enhanced structural and environmental stability alongside superior electrochemical performance. Among the numerous candidates, the ML and DFT-driven strategies highlight P3-type K0.3Mn0.9Cu0.1O2 (KMCO) as a promising high-performance KIB cathode. Experimental validation confirms that the KMCO cathode significantly improves K-storage properties, exhibiting high-power density and cycling stability even after four weeks of air exposure. This study opens new pathways for discovering and developing suitable electrode materials for next-generation battery applications.