Calculations of various properties, such as the formation energies and the voltage profiles, are performed to justify the DP model. Next, we study the dynamic behavior of the oxygen dimer formation induced by the TM cation migrations in the CoO2 by DeePMD simulations combined with enhanced sampling techniques. Finally, based on the mechanistic insights provided by the DeePMD results, the effect of Ti dopants on the kinetic properties of the key steps is investigated. We aim to construct a potential surface energy (PES) by fitting the data set labeled by density functional theory (DFT) calculations via a deep neural network. In order to obtain a robust PES, we need to ensure that all of the systems used for training are on the ground-state PES with respect to the degree of freedom of the TM ions' spin states.