Enhancement of stability and electronic properties of ultra-high nickel cathodes by aluminium and boron codoping studied by combined density functional theory and neural network models.

The rapid growth of electric vehicles and large-scale energy storage systems has intensified research on nickel-rich lithium-ion battery cathodes, prized for their high energy density, superior electrochemical performance, and reduced material costs. In this study, density functional theory (DFT) calculations are integrated with machine learning (ML) techniques using DeePMD-based neural network models to investigate the structural stability and electronic properties of Ni-rich cathode materials, including LiNi0.6Mn0.2Co0.2O2 (NMC622), LiNi0.8Mn0.1Co0.1O2 (NMC811), and LiNi0.9Co0.05Mn0.05O2 (NMC955). The neural network models successfully reproduce DFT-calculated energies and forces with R 2 values exceeding 0.9, enabling accurate prediction of thermodynamic stability across wide temperature ranges. Increasing Ni content enhances electronic conductivity but reduces structural robustness due to intensified Ni-O hybridization. Co-doping NMC955 with aluminum and boron improves lattice stability, redistributes charge, and lowers the Li-ion diffusion barrier from 0.355 eV to 0.206 eV. Thus, Al-B co-doping achieves a favorable balance between electronic and structural performance. This integrated DFT-ML framework offers an efficient pathway for the rational design of robust, high-energy Ni-rich cathodes for next-generation lithium-ion batteries.