Machine-Learning Approach for Predicting the Discharging Capacities of Doped Lithium Nickel–Cobalt–Manganese Cathode Materials in Li-Ion Batteries

, respectively, against a hold-out test set. Furthermore, a game-theory-based variable-importance analysis reveals that doped NCM materials with higher lithium content, smaller dopant content, and lower-electronegativity atoms as the dopant are more likely to possess higher IC and EC. This study has demonstrated the exciting potentials of applying cutting-edge machine-learning techniques to accurately capture the complex structure-property relationship of doped NCM systems, and the models can be used as fast screening tools for new doped NCM structures with more superior electrochemical discharging properties.