过渡金属集群的结构确定和层次进化基于一个改进的自适应差异进化与邻近搜索算法
Wei-Hua Yang1, Fang-Qi Yu1, Rao Huang1
1Department of Physics, Xiamen University, Xiamen 361005, China.
Journal of chemical information and modeling
|October 19, 2023
概括
本研究引入了一种新的算法,用于预测过渡金属集群的最低能量结构. 它揭示了不同尺寸的,白金和铁集群的明显生长模式和稳定配置.
科学领域:
- 计算化学和物理计算化学和物理
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
背景情况:
- 确定过渡金属集群的最佳结构和层次演变对于应用至关重要.
- 大型原子集群的全球优化在物理学和化学中构成了重大挑战.
研究的目的:
- 采用高效率的自适应差异演化与邻近搜索 (SaNSDE) 算法进行全球优化.
- 研究 (Co),白金 (Pt) 和铁 (Fe) 集群的结构和能量特性 (N = 3-200).
- 为了阐明这些集群的结构演变和生长模式作为大小的函数.
主要方法:
- 利用了自适应差异演变与邻近搜索 (SaNSDE) 算法,结合了优化的交叉操作和盆地跳跃.
- 对剑桥集群数据库 (CCD) 进行算法性能评估.
- 采用分析方法系统地研究结构和能量特性,重点关注形状,原子排列,相似性和生长.
主要成果:
- 与CCD相比,在Fe集群中发现了13个新的低能结构.
- 确定了Co,Pt和Fe集群的独特的魔法数字和高度对称的稳定结构.
- 具有独特的生长趋势:Co的二面体,Pt的十面体和Fe的二面体环形,有外结构和hcp,fcc,bcc等配置的证据.
结论:
- SaNSDE算法有效预测低能集群结构,推进全球优化技术.
- 建立了Co,Pt和Fe集群的一般增长趋势,不同于简单的原子对原子的加法.
- 提供了关于过渡金属集群的结构演变和稳定性的基本见解,这对于它们的目标应用至关重要.
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