Bond-Strength-Based Understanding of Oxygen Vacancy Migration Barriers in Rutile Oxides

TL;DR

This study combines DFT and bond-valence models to analyze and predict oxygen vacancy migration barriers in rutile oxides, decomposing bonding contributions and establishing efficient estimation methods.

Contribution

It introduces a bond-strength based approach that explicitly quantifies covalent and ionic bonding contributions to vacancy migration barriers, enabling accurate predictions across series.

Findings

Strong correlation between bond contributions and DFT migration barriers

Average bond strength parameter estimates vacancy migration barriers effectively

Two fitted parameters allow efficient prediction of oxygen vacancy barriers

Abstract

We carry out bond-strength based analysis for the migration barrier ($E_{\rm B}$) of oxygen vacancies in rutile-type 3$d$ transition-metal dioxides by combining density-functional theory (DFT) and the bond-valence model. The covalent and ionic contributions to chemical bonding are explicitly decomposed and quantified by the sum of the integrated crystal orbital Hamilton population ($S_c$) and the Madelung energy ($S_i$), respectively. Both $S_c$ and $S_i$ exhibit strong correlations with the $E_{\rm B}$ from DFT ($E_{\rm B}^{\rm DFT}$), and their average $\bar{S}$ provides a reasonable estimate of $E_{\rm B}^{\rm DFT}$ across the oxide series. Inspired by the bond-valence model, two parameters are extracted by fitting to a large dataset of 3$d$ transition-metal dioxides. Our results show that using these parameters, $E_{\rm B}$ of oxygen vacancies can be efficiently estimated.