Kinetic Monte Carlo simulations of ionic conductivity in oxygen ion conductors

Ionic conductivities of solid-state materials are crucial for the performance of various applications ranging from batteries and fuel cells to resistive switching devices. The macroscopic ionic conductivity results directly from the microscopic energy landscape of ion diffusion. Lattice site energies and migration barriers depend on lattice defects such as vacancies and dopant ions in the local environment. The multiplicity of possible defect interactions with the migrating ion impedes the use of analytic models. While ab initio methods allow the calculation of the microscopic energy barriers for individual jumps, calculations of the macroscopic conductivity are computational very demanding, especially for more than 250 different materials and their possible ionic configurations as presented in this study. Kinetic Monte Carlo simulations allow the simulation of the ionic conductivity based on ab initio data and bridge the gap between microscopic jump events and the macroscopic conductivity. In this work, we discuss the Kinetic Monte Carlo method and its application to oxygen ion conductors for the example of doped ceria. We demonstrate how Kinetic Monte Carlo simulations can be accelerated to be 100 times faster with preserved high accuracy. Moreover, we report how the accuracy of Kinetic Monte Carlo simulations is improved with a large interaction radius and minimal computational expenses.

Automated summary

Ionic conductivities of solid-state materials depend on the microscopic energy landscape of ion diffusion, influenced by lattice defects such as vacancies and dopant ions. The complexity of defect interactions with migrating ions hinders the use of analytic models. Kinetic Monte Carlo simulations offer a way to bridge the gap between microscopic jump events and macroscopic conductivity, allowing for accelerated and accurate simulations.