Computational Study Of Grain Structure Evolution In Cdte/cds Via Molecular Dynamics

Grain structure analysis plays an important role in the identification of grain boundary characteristics, which can affect the efficiency of Cadmium Telluride/Cadmium Sulfide (CdTe/CdS) solar cells since they can act as recombination centers for carriers. Computer simulations such as molecular dynamics (MD) can be a very convenient and cost- effective method of investigating the growth evolution and grain structure of materials. The recently reported and experimentally validated MD simulated growth of polycrystalline CdTe/CdS films shows that these materials mostly consist of zinc blende (ZB) and wurtzite (WZ) structures, along with highly disordered atoms. However, little information about the semiconductor compound grain structure quantification and evolution has been reported in the literature. In this dissertation, several computational tools were used to analyze the formation and behavior of grains and grain boundaries in polycrystalline CdTe/CdS structures. A computational approach was applied to analyze the CdTe/CdS films obtained from our molecular dynamics simulations. It was demonstrated that by focusing on ZB and WZ structures, or even cation and anion sublattices of the tetragonal crystal structure of the compound, the parameters obtained from the centrosymmetric, polyhedral template matching and common neighbor analyses can be used to calculate the orientation of each atom in the grain tracking algorithm. This provides a variety of useful information, such as grain domains, grain orientations, and sample texture. Furthermore, microstructure evolution was performed to understand grain growth mechanisms and kinetics. There are other useful features that are not included in the current tool, such as identification and tracking of point defects â?? especially vacancies at grain boundaries. Nonetheless, the current approach is useful and our CdTe/CdS results provide inputs for further computational studies to relate grain structures to physical, chemical, mechanical, and electronic properties. Moreover, dynamic machine learning models of structure evolution could be developed using these identified features through an automated procedure