Application of a Parallel Genetic Algorithm to the Global Optimization of Gas-Phase and Supported Gold-Iridium Sub-Nanoalloys
Application of a Parallel Genetic Algorithm to the Global Optimization of Gas-Phase and Supported Gold-Iridium Sub-Nanoalloys
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DOI：
10.1021/acs.jpcc.5b10226
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发表时间：
2016-02-25
影响因子：
3.7
通讯作者：
Johnston, Roy L.
中科院分区：
文献类型：
--
作者：
Davis, Jack B. A.;Horswell, Sarah L.;Johnston, Roy L.
采用Birmingham并行遗传算法（BPGA）对MgO（100）支撑的AuIr亚纳米合金进行了直接密度泛函全局优化。BPGA是一种基于池的遗传算法，用于纳米合金的结构表征。在BPGA中使用的并行池方法允许代码在MgO（100）表面的存在下表征N = 4-6 AunIrN-n团簇的结构。密度泛函理论的使用允许代码捕获系统中的量子尺寸效应，这决定了它们的结构。搜索结果显示，在结构和化学有序的表面支持和气相的全球最小结构之间的显着差异。
The direct density functional theory global optimization of MgO(100)-supported AuIr sub-nanoalloys is performed using the Birmingham parallel genetic algorithm (BPGA). The BPGA is a pool-based genetic algorithm for the structural characterization of nanoalloys. The parallel pool methodology utilized within the BPGA allows the code to characterize the structures of N = 4-6 AunIrN-n clusters in the presence of the MgO(100) surface. The use of density functional theory allows the code to capture quantum size effects in the systems, which determine their structures. The searches reveal significant differences in structure and chemical ordering between the surface-supported and gas-phase global minimum structures.