Computational Investigation of the Effect of Nitrogen Dopants and Oxygen Vacancies on the Energetics of Lithium Lanthanum Titanates

Abstract

Doping is a powerful strategy for improving the ionic conductivity of ceramic-type solid-state ion conductors. Compared to cation doping, anion doping is much less studied but has been shown to improve Li-ion transport in perovskite lithium lanthanum titanates (LLTOs). In this work, the structure and energetics of nitrogen-doped LLTOs were studied by using first-principles density functional theory calculations. The calculations found a high energy cost for nitrogen doping, which decreases with the introduction of oxygen vacancies or with the formation of nitrogen–nitrogen and nitrogen–oxygen dimers. Dimer formation reflects potentially significant structural distortions. Six machine learning models, including four descriptor-based models (multiple linear regression, random forest, support vector machine, and XGBoost) and two graph-based neural network models (CGCNN and MEGNet), were evaluated for predicting the energy of both unoptimized and DFT-optimized LLTO structures. XGBoost and MEGNet were found to be the best-performing models from the two categories, both exhibiting correlation coefficients larger than 0.99. SHAP analysis shows that oxygen vacancies prefer to form near La³⁺ ions, while the close proximity of Li⁺ and vacancies has a destabilizing effect. The latter suggests that thermodynamically Li⁺ may be repelled from the oxygen vacancy centers and thus be unable to directly benefit from the potential advantages of vacancies for Li⁺-ion transport. These results offer detailed insights into the stability of various anion-doped LLTOs and the interplay of various structural motifs in impacting ion conduction.