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    Atomic structure prediction of nanoclusters by evolutionary algorithm

    时间:2017年06月16日    来源:    作者:    阅读:

    报告人: Renat Sabirianov 教授 (University of Nebraska at Omaha)

    报告时间:2017年617日(星期六)  15:00

    报告地点:知新楼 C1111

    邀请人:康仕寿 教授

    报告简介:

    The problem of computational crystal structure prediction will be presented. I will discuss the application of evolutionary algorithm to predict properties of nanostructures of transition metals and their alloys. Specifically, the search for metastable structures of M13 shows several low symmetry structures that would not be expected from the known bulk structures. Further, the rare earth free magnetic materials that exhibit a high room-temperature energy product will be explored using the evolutionary algorithm coupled with density functional theory (DFT) is used to identify the global energy minimum atomic structures of Zr-Co and Hf-Co clusters. Using evolutionary crystal structure optimization algorithm, as implemented in USPEX, we studied the atomic structure, binding energies, magnetic properties, and anisotropy of ZrxCoy and HfxCoy(x=1,2 and y=5,7,11) clusters. A set of metastable and global minimum atomic structures are identified. Several new lower energy configurations were identified for. I will discuss the magnetic interaction between the atoms of the clusters which is critical in finding the lowest energy structure. Our calculations show that Zr-Co and Hf-Co clusters have ferromagnetic coupling and large magnetization and magnetocrystalline anisotropy energies.