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

SUBMITTER: Wang G 

PROVIDER: S-EPMC8461773 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature