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) Ge 12 C 12 and Si 12 C 12 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 pristine Si 12 C 12 nanocage brilliantly exhibited the highest V cell (1.49 V) and theoretical capacity (668.42 mAh g − 1 ) among the investigated nanocages and, hence, the most suitable negative electrode material for lithium-ion batteries. Moreover, we utilized four machine learning regression algorithms, namely, Linear, Lasso, Ridge, and ElasticNet regression, to predict the V cell of the nanocages obtained from DFT simulation, achieving R 2 scores close to 1 (R 2 = 0.99) and lower RMSE values (RMSE < 0.05). Among the regression algorithms, Lasso regression demonstrated the best performance in predicting the V cell of the nanocages, owing to its L1 regularization technique.