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利用Gupta势结合遗传算法研究ConCu55-n(n=0—55)混合团簇的结构演化及基态能量
引用本文:鄂箫亮,段海明.利用Gupta势结合遗传算法研究ConCu55-n(n=0—55)混合团簇的结构演化及基态能量[J].物理学报,2010,59(8):5672-5680.
作者姓名:鄂箫亮  段海明
作者单位:新疆大学物理科学与技术学院,乌鲁木齐 830046
基金项目:国家自然科学基金(批准号:10864005),教育部留学回国人员科研启动基金资助的课题.
摘    要:采用半经验的Gupta多体势结合遗传算法对ConCu55-n(n=0—55)混合团簇的基态结构和能量进行了研究,发现这些混合团簇的基态结构是在Co55,Cu55单质团簇(Mackay二十面体)的基础之上发生的畸变;从n=0(Cu55)开始,Co原子从中心到表面,从棱到顶点依次、连续替换Cu原子;基态结构与键能较大键的数目及其平均键长有关;Co13Cu42具有最稳定的结构,13个Co原子全部位于团簇内部形成Mackay二十面体对整个团簇的稳定性有显著影响. 关键词: 团簇 结构和能量 Gupta势 遗传算法

关 键 词:团簇  结构和能量  Gupta势  遗传算法
收稿时间:2009-09-13

Study of the structure evolution and ground state energy of ConCu55-n(n =0-55)bimetallic clusters by using the Gupta potential combined with a genetic algorithm
E Xiao-Liang,Duan Hai-Ming.Study of the structure evolution and ground state energy of ConCu55-n(n =0-55)bimetallic clusters by using the Gupta potential combined with a genetic algorithm[J].Acta Physica Sinica,2010,59(8):5672-5680.
Authors:E Xiao-Liang  Duan Hai-Ming
Institution:College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China;College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
Abstract:The ground-state structures and energies of the bimetallic clusters ConCu55-n(n=0—55) were obtained by using the semi-empirical Gupta potential combined with a genetic algorithm. The ground-state geometries of those bimetallic clusters can be characterized by the basic Mackay icosahedron of the pure Co55 (Cu55) cluster. Beginning with n=0 (Cu55), the Cu atom(s) is(are) replaced in order and continuously by the Co atom(s) from the center to the surface and from the edge to the vertex. The ground-state structure depends on the number of the Co—Co bonds which have the largest energy, the number of the Co-Cu bonds with large energy and the average bond length of those bonds. Co13Cu42 is a magic cluster with an icosahedral core formed by 13 Co atoms, which plays a key role in the high stability of those bimetallic clusters.
Keywords:clusters  structure and energy  Gupta potential  genetic algorithm
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