Genetic algorithm assisted multiscale modeling of grain boundary segregation of Al in ZnO and its correlation with nominal dopant concentration

Grain boundary (GB) segregation of Al in ZnO plays an important role in lowering its thermal conductivity for thermoelectric applications. However, the effect of Al concentration on the GB complexions and their transition is not well understood. Herein, a genetic algorithm assisted multiscale modelling framework was used to study the role of GB concentration on the GB segregation of Al on five special twin GBs of ZnO. A critical concentration of 5-6 atoms/nm2 was determined for the complexion transition from single layer to multilayer. Calculated segregation energies were used in a phenomenological model to link GB concentration with the nominal concentration of dopants. The model was used to calculate the nominal solubility of Al in ZnO as a function of grain size, which was validated with the experimental data from the literature. The proposed framework provides a path for establishing GB-structure - property correlation and thereby, predictive dopant engineering of ceramics.

Automated summary

This study investigated the segregation of Al in ZnO grain boundaries (GBs) and established the correlation between GB concentration and nominal dopant concentration using a genetic algorithm assisted multiscale modelling framework. The nominal solubility of Al in ZnO was also calculated as a function of grain size. These findings provide a pathway for predictive dopant engineering and understanding the relationship between GB structure and properties in ceramics.