Air stable layered oxide cathode materials assisted by machine learning for K-ion batteries
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Layered oxides utilizing KxMnO2 as the cathode material for potassium ion batteries have drawn considerable interest as they offer high energy density and stability. However, a challenge with KxMnO2 cathodes is their rapid oxidation and hydration when exposed to air. Thus, the development of layered cathodes with improved air stability and high energy density is essential. To address this issue, we have employed machine learning (ML) algorithms to predict the crystal stability of cathode materials and validated the results through DFT. Through this combination of ML and DFT, we successfully synthesized K0.3Mn0.9Cu0.1O2 by partially substituting Cu2+ in K0.3MnO2, resulting in excellent electrochemical performance and remarkable air stability.
References [1] H. Kim, D. H. Seo, J. C. Kim, S. H. Bo, L. Liu, T. Shi, G. Ceder, Adv. Mater. 2017, 29, 1702480.