Kinetic Monte Carlo simulations of ionic conductivity in oxygen ion conductors

2021 ◽  
Vol 257 ◽  
pp. 123767
Author(s):  
Philipp Hein ◽  
Benjamin O.H. Grope ◽  
Julius Koettgen ◽  
Steffen Grieshammer ◽  
Manfred Martin
Keyword(s):  
Monte Carlo ◽  
Ionic Conductivity ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Oxygen Ion ◽  
Oxygen Ion Conductors ◽  
Kinetic Monte Carlo Simulations ◽  
Ion Conductors

Kinetic Monte Carlo Simulations for Solid State Ionics: Case Studies with the MOCASSIN Program

2021 ◽  
Vol 29 ◽  
pp. 117-142
Author(s):  
Steffen Grieshammer ◽  
Sebastian Eisele
Keyword(s):  
Monte Carlo ◽  
Solid State ◽  
Ionic Conductivity ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Model Systems ◽  
Solid State Ionics ◽  
Kinetic Monte Carlo Simulations ◽  
Defect Interactions ◽  
The Impact

Kinetic Monte Carlo simulations are a useful tool to predict and analyze the ionic conductivity in crystalline materials. We present here the basic functionalities and capabilities of our recently published Monte Carlo software for solid state ionics called MOCASSIN, exemplified by simulations of several model systems and real materials. We address the simulation of tracer correlation factors for various structures, the correlation in systems with complex migration mechanisms like interstitialcy or vehicle transport, and the impact of defect interactions on ionic conductivity. Simulations of real materials include a review of oxygen vacancy migration in doped ceria, oxygen interstitial migration in La-rich melilites, and proton conduction in acceptor doped fully hydrated barium zirconate. The results reveal the impact of defect interactions on the ionic conductivity and the importance of the defect distribution. Combinations of these effects can lead to unexpected transport behavior in solid state ionic materials, especially for multiple mobile species. Kinetic Monte Carlo simulations are therefore useful to interpret experimental data which shows unexpected behavior regarding the dependence on temperature and composition.

Understanding the ionic conductivity maximum in doped ceria: trapping and blocking

2018 ◽  
Vol 20 (21) ◽  
pp. 14291-14321 ◽  
Author(s):  
Julius Koettgen ◽  
Steffen Grieshammer ◽  
Philipp Hein ◽  
Benjamin O. H. Grope ◽  
Masanobu Nakayama ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ionic Conductivity ◽  
Dft Calculations ◽  
Monte Carlo Simulations ◽  
First Principles ◽  
Kinetic Monte Carlo ◽  
Doped Ceria ◽  
Kinetic Monte Carlo Simulations

Ionic conductivity is systematically predicted by only a few migration energies from first-principles DFT calculations combined with Kinetic Monte Carlo simulations.

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