Evaluation of the ionic conductivity of cation electrolytes by Kinetic Monte Carlo simulations

电池和燃料电池可以有效地存储电能，对于能量转换和电动汽车的进步是不可避免的。在固体氧化物燃料电池和固态电池中，陶瓷电解液起着至关重要的作用。因此，需要开发和优化电解液材料来提高效率、延长寿命和降低器件成本。过去人们研究了各种各样的可能的结构和成分，但化合物的绝对数量使所有可能性的实验研究变得复杂。在原子水平上，材料的性质可以用密度泛函理论来研究，这种方法在最近几年得到了越来越多的重视，主要是由于计算资源的可获得性。利用动力学蒙特卡罗模拟，通过模拟移动载流子在晶格中的运动，将微观水平上的能量和过程应用到材料的宏观离子电导中。蒙特卡罗模拟中所需的能量参数将从文献中获得，并由自己的计算补充。该项目由四个步骤组成。第一步，从文献中确定有希望的阳离子导体并收集能量参数。在第二步中，这些能量由自己的计算补充，并在第三步中用于创建能量模型。第四步，将能量模型应用于动力学蒙特卡罗模拟，得到与温度和组成有关的离子电导率，其目的是了解和预测离子电导率。一方面，对离子导电机理以及组成、结构与离子导电性之间的关系有了更深入的认识。另一方面，该项目旨在预测具有高离子导电性的材料，并优化成分。

Batteries and fuel cells allow the efficient storage of electrical energy and are inevitable for the energy transition and advancement of electromobility. In solid oxide fuel cells and solid state batteries, the ceramic electrolyte plays a crucial role. The development and optimization of electrolyte materials is thus required to increase efficiency, extend lifetime and lower cost of the devices.A wide variety of possible structures and compositions has been investigated in the past, but the sheer number of compounds complicates the experimental investigation of all possibilities. On an atomic level, material’s properties can be investigated by density functional theory and this approach has gained increasing importance in recent years, mainly due to the availability of computational resources. Using Kinetic Monte Carlo simulation, the energies and processes on the microscopic level can be applied to obtain the macroscopic ionic conductivity of the material by simulating the motion of mobile charge carriers through the lattice.In the present project, the ionic conductivity of selected cation conductors for Li+, Na+ and H+ ions will be investigated by combining density functional theory and Kinetic Monte Carlo simulations. The energy parameters, required in Monte Carlo simulations, will be obtained from literature and complemented by own calculations. The project is composed of four steps. In the first step, promising cation conductors are identified and energy parameters are collected from literature. In the second step, these energies are complemented by own calculations and used to create energy models in the third step. In the fourth step, the energy models are applied in Kinetic Monte Carlo simulations to obtain the ionic conductivity depending on temperature and composition.The goal of the project is the understanding and prediction of ionic conductivity. On the one hand side, a deeper insight into ionic conduction mechanisms and relation between composition, structure and ionic conductivity should be obtained. On the other hand, the project aims to predict materials with high ionic conductivity and optimize the composition.