Machine learning-assisted DFT-prediction of pristine and endohedral doped (O and Se) Ge₁₂C₁₂ and Si₁₂C₁₂ nanostructures as anode materials for lithium-ion batteries.

ABSTRACT: Nanostructured materials have gained significant attention as anode material in rechargeable lithium-ion batteries due to their large surface-to-volume ratio and efficient lithium-ion intercalation. Herein, we systematically investigated the electronic and electrochemical performance of pristine and endohedral doped (O and Se) Ge₁₂C₁₂ and Si₁₂C₁₂ nanocages as a prospective negative electrode for lithium-ion batteries using high-level density functional theory at the DFT/B3LYP-GD3(BJ)/6-311 + G(d, p)/GEN/LanL2DZ level of theory. Key findings from frontier molecular orbital (FMO) and density of states (DOS) revealed that endohedral doping of the studied nanocages with O and Se tremendously enhances their electrical conductivity. Furthermore, the pr

SUBMITTER: Egemonye TC 

PROVIDER: S-EPMC11528116 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature