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储存电能，是能源转换和电动汽车发展的必然选择。在固体氧化物燃料电池和固态电池中，陶瓷电解质起着至关重要的作用。因此，需要开发和优化电解质材料，以提高效率，延长使用寿命和降低设备成本。过去已经研究了各种各样可能的结构和组成，但是化合物的数量之多使对所有可能性的实验研究变得复杂。在原子水平上，材料的性质可以通过密度泛函理论来研究，这种方法近年来越来越重要，主要是由于计算资源的可用性。利用动力学蒙特卡罗模拟，可以利用微观层面上的能量和过程，通过模拟移动载流子通过晶格的运动，得到材料的宏观离子电导率。在本项目中，将结合密度泛函理论和动力学蒙特卡罗模拟，研究选定阳离子导体对Li+、Na+和H+离子的离子电导率。蒙特卡罗模拟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。在第一步中，从文献中识别有前途的阳离子导体并收集能量参数。在第二步中，这些能量被自己的计算补充，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，得到离子电导率随温度和组成的变化。该项目的目标是了解和预测离子电导率。一方面，需要更深入地了解离子传导机理以及组成、结构与离子电导率之间的关系。另一方面，该项目旨在预测具有高离子电导率的材料并优化其组成。

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.