Abstract
Spinel structures with various porosities are promising ion batte cathodes for enhancing the performance of electrode materials. Based on the machine learning method, we performed a comprehensive screening of all spinel structures from the periodic table and identified the best Mg/Zn ion battery cathode materials with a high conductivity and rapid ion kinetics, with a prediction accuracy of 91.2%. We used a target-driven XGBoost algorithm to accelerate the ab initio predictions and reported six new spinel structures (MgNi2O4, MgMo2S4, MgCu2S4, ZnCa2S4, ZnCu2O4, and ZnNi2O4) with high electronic conductivities, high ion diffusions (>1 × 10−9 cm2s−1), low volume expansions (<22%) and thermal stability at room temperature, which served as the best cathodes for Mg/Zn ion batteries. Among them, MgNi2O4, MgMo2S4, MgCu2S4, ZnCu2O4 and ZnNi2O4 were predicted to be five new superionic conductors with an exceptionally high ionic conductivity (>10−4 S·cm−1) at room temperature. The proposed strategy shortens the research cycle of spinel screening for cathodes of Mg/Zn ion batteries and offers a solution toward the design of high-performance 3D electrode materials.
Published Version
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