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.

ABSTRACT: 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 LiNi_0.6Mn_0.2Co_0.2O₂ (NMC622), LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811), and LiNi_0.9Co_0.05Mn_0.05O₂ (NMC955). The neural network models successfully reproduce DFT-calculat

SUBMITTER: Vo Anh Duy N 

PROVIDER: S-EPMC12679595 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature