无偏模糊全局优化 Lennard-Jones 团簇，N ≤ 1000。

Unbiased fuzzy global optimization of Lennard-Jones clusters for N ≤ 1000.

机构信息

Center for Chemistry of Novel & High-Performance Materials, Department of Chemistry, Zhejiang University, Hangzhou 310027, China.

Hangzhou Institute of Advanced Studies, Zhejiang Normal University, 1108 Gengwen Road, Hangzhou 311231, China.

出版信息

J Chem Phys. 2019 Dec 9;151(21):214105. doi: 10.1063/1.5127913.

DOI:10.1063/1.5127913

PMID:31822070

Abstract

We propose a fuzzy global optimization (FGO) algorithm to identify the lowest-energy structure of nanoclusters. In contrast to traditional methods implemented in the real space, FGO utilizes mostly the discrete space in a fuzzy search framework. Starting from random initial configurations, we carry out directed Monte Carlo and surface Monte Carlo in the discrete space to obtain low-energy candidate clusters and make real-space local optimizations finally to get the real global minimum structure. The performance of FGO is demonstrated in a large set of standard Lennard-Jones (LJ) clusters with up to 1000 atoms. All the putative global minima reported in the literature are successfully obtained with a low scaling of CPU time with cluster size, and new global minimum structures for LJ clusters with 894, 974, and 991 atoms are identified. Due to the unbiased nature, FGO can potentially deal with the global optimization of other nanomaterials with high efficiency and reliability.

摘要

我们提出了一种模糊全局优化（FGO）算法来确定纳米团簇的最低能量结构。与在实空间中实现的传统方法不同，FGO 在模糊搜索框架中主要利用离散空间。从随机初始构型开始，我们在离散空间中进行有方向的蒙特卡罗和表面蒙特卡罗，以获得低能候选团簇，并最终进行实空间局部优化，以获得真正的全局最小结构。FGO 的性能在一组具有多达 1000 个原子的标准 Lennard-Jones（LJ）团簇中得到了验证。文献中报道的所有假定的全局最小值都成功获得，并且随着团簇大小的增加，CPU 时间的比例较低，还确定了具有 894、974 和 991 个原子的 LJ 团簇的新全局最小结构。由于其无偏的性质，FGO 有可能以高效率和可靠性处理其他纳米材料的全局优化。