基于粒子群优化算法的团簇结构预测方法与应用

项目名称： 基于粒子群优化算法的团簇结构预测方法与应用

项目编号： No.11274136

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 数理科学和化学

项目作者： 马琰铭

作者单位： 吉林大学

项目金额： 88万元

中文摘要： 团簇结构的确定是团簇研究的关键问题。完全通过实验确定团簇结构存在着困难和挑战，发展高效的团簇结构预测方法至关重要。早期团簇结构预测主要集中在基于单一初始结构演化的跃迁势垒方法和基于群体搜索的基因遗传算法。两种算法都有成功的范例，但都有局限性，如前者对人为选择的初始结构具有依赖性，难以克服高能量势垒，后者难以保持群体的多样性，易陷入结构早熟，处理大体系有困难。粒子群优化算法是依据鸟类捕食的仿生学原理建立起来的基于种群搜索策略的多目标全局优化算法。项目组前期将该算法成功应用于三维晶体的结构预测，发展并建立了卡利普索（CALYPSO）结构预测方法和软件，现已经成为晶体结构预测领域的重要方法。本项目拟基于前期积累，发展基于粒子群优化算法的零维团簇结构预测方法和技术，编制具有自主知识产权的团簇结构预测程序，并集成于CALYPSO软件包，开展若干团簇体系的结构设计工作，获得对团簇物理和化学的新认知。

中文关键词： 粒子群优化算法；团簇；结构预测；；

英文摘要： Cluster structure determination is the key problem in the cluster research. The sole use of experimental measurement to determine cluster structure remains a great difficulty and a big challenge. Here, the development of effective method on cluster structure prediction is crucial. Currently, there are two main methodologies on cluster structure prediction: （i）the method to overcome the energy barrier based on the guessed initial structures and (ii) genetic algorithm based on the group searches. The first kind of methods heavily depends on the initial structures and is not able to overcome the very large energy barrier, while genetic algorithm may allow structures trapped in the local minimum and has the problem of structural diversity and the difficulty in handling the large systems. Particle swarm optimization is inspired by the birds flock and is a multiple target algorithm. Our team has earlier applied particle swarm optimization into the prediction of three dimensional crystal structures and developed the CALYPSO structural prediction technique. CALYPSO methodology has become one of major structural prediciton methods. The current project is trying to apply particle swarm optimization algorithm into the zero-dimensional cluster structure prediction and code the developed method into the CALYPSO software. Lat

英文关键词： particle swarm optimization algorithm；cluster；structure prediction；；