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

Abstract

Understanding the governing dopant feature for cyclic discharge capacity is vital for the design and discovery of new doped lithium nickel-cobalt-manganese (NCM) oxide cathodes for lithium-ion battery applications. We herein apply six machine-learning regression algorithms to study the correlations of the structural, elemental features of 168 distinct doped NCM systems with their respective initial discharge capacity (IC) and 50th cycle discharge capacity (EC). First, a Pearson correlation coefficient study suggests that the lithium content ratio is highly correlated to both discharge capacity variables. Among all six regression algorithms, gradient boosting models have demonstrated the best prediction power for both IC and EC, with the root-mean-square errors calculated to be 16.66 mAhg-1and 18.59 mAhg-1, 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.