Nin(n=3～39)团簇结构，能量和稳定性的研究

The stable geometric structure and energy of Ni_n(n=3～39) clusters as a function of cluster size are studied by the Monte Carlo simulation. The interaction among atoms is calculated through Lennard-Jones plus Axilrod-Teller potentials. It is found that the clusters grow through adding atoms on one or more surfaces of Ni₇ or Ni₁₃ after the cluster size n is larger than 7. It is also found that there exists direct correlation between the stability and geometrical structures of clusters. Relatively, highly symmetry clusters are more stable. In addition, the nickel clusters with fcc-like structure such as Ni₃₃, Ni₃₆ and Ni₃₈ are more stable than their neighboring clusters.     

(SiO2)n团簇的基态能量和结构

应用遗传算法对二氧化硅团簇（SiO2）n（n≤20）的结构进行了优化计算。分析讨论了结构和结合能随团簇尺寸的变化规律,发现（SiO2）n团簇系列不存在明显的幻数,并在n≤20的范围内,不呈现出相应大块物质的结构特征,     

新团簇形成的动力学研究

利用紧束缚模型,通过对不同初始条件下的碱金属团簇的碰撞研究,得到形成大分子团簇的可能性.结果表明,对同一种团簇,具有空间对称性的团簇间的碰撞更易形成大团簇,而平面结构的团簇碰撞则反之,并且空间结构的团簇碰撞形成的新团簇复合体更加稳定.另一方面,团簇碰撞过程是可逆的.     

杂质对硬球系统团簇成核临界条件的影响

通过CS状态方程及熵极大原理在多元胶体系统研究了杂质对硬球系统团簇成核临界条件的影响. 极少量元素或杂质的小胶球对系统成核的临界条件的影响也是不可忽略的： 成核的临界体积分量显著地降低;此外,研究还发现杂质对成核的临界尺寸影响较少.     

团簇研究中的原子间势函数

原子间势函数在团簇结构和性质的理论研究中十分重要.本文将模拟团簇原子间相互作用的势函数划分成三类,对各类势函数的发展状况进行了综述;并对原子间势函数与最优化方法和分子动力学相结合,在团簇结构和性能研究中的应用和发展方向进行了讨论.     

铜氧团簇负离子的产生

自Smalley等利用激光蒸发／超声分子束载带（ix／un）法产生c。。问起，这种方法逐渐成为形成高质量气相团簇的常用实验手段，其中分子束载带的主要作用是缓冲气体通过提供三体碰撞稳定动力学激发的团簇并促进高质量团簇的形成．但0’Keef6门和Cre。Sy同的实验发现无需利用缓冲     

铍小团簇的基态能量与拓扑指数的相关性

本文对铍小团簇（Bem,2≤m≤25）的基态能量分别与其价电子能级连接性指数、路径数和Wiener指数的相关性作了探讨。对基态能量E的估算结果令人满意。     

银和硫团簇的反应

研究气相中原子或团簇的化学反应可以使我们从分子水平上研究化学反应的机理．激光溅射固体样品产生团簇,进而研究所形成团族的化学反应是研究团簇反应的一种方法．用高强度激光使固体样品气化,气化物彼此碰撞反应并在真空中膨胀冷却形成团簇和团簇离子,这一类反应是成...     

双二十面体Agn(n=19, 23, 24, 25)团簇低能稳态构型的TS搜索

采用基于NEB的MEP的LST/QST方法, 发展了一种不依赖团簇初始结构而得到其低能稳态构型的过渡态搜索方法, 预测和分析了孤立与铰链双二十面体Ag_n (n=13, 19, 23, 24, 25)的低能稳态构型. 证实Ag₁₃-I_h的低能稳态构型是一个含有五角双锥碎片的Ag₁₃-C₂扁平层状结构, 而Ag₁₉-D_5h的低能稳态构型则是在13原子二十面体上添加6原子组成Ag₁₉-C₁球形结构. 并且揭示Ag₂₃-D_3h, Ag₂₄-D_2h, Ag₂₅-D_5d与Ag_n (n=17～22)团簇一样, 其低能稳态构型也含有一个13原子的变形二十面体核心.     

杂合型全局优化法优化水分子团簇结构

基于遗传算法、快速模拟退火及共轭梯度方法提出了一种快速的杂合型全局优化方法(fast hybrid global optimization algorithm, FHGOA),并将这一方法应用于TIP3P和TIPS2模型水分子团簇(H2O)n结构的优化.在进行TIP3P模型水分子团簇结构的优化过程中,发现了能量比文献值更低的团簇结构,且执行效率有较大提高.把该方法应用到优化TIPS2模型的水分子团簇,发现最优结构和采用TTM2-F模型优化的水分子团簇结构在n < 17时完全相同,为全表面结构;而在n=17、19、22时为单中心水分子笼状结构;在n=25、27时为双中心水分子笼状结构.说明随着团簇中水分子个数的增加,采用TIPS2和TTM2-F势能函数优化的团簇最优结构有相同的变化趋势.     

Prediction of the lowest energy configuration for Lennard-Jones clusters

Based on the work of previous researchers, a new unbiased optimization algorithm—the dynamic lattice searching method with two-phase local search and interior operation (DLS-TPIO)—is proposed in this paper. This algorithm is applied to the optimization of Lennard-Jones (LJ) clusters with N=2–650, 660, and 665–680. For each case, the putative global minimum reported in the Cambridge Cluster Database (CCD) is successfully found. Furthermore, for LJ533 and LJ536, the potential energies obtained in this study a...     

A dynamic lattice searching method with interior operation for unbiased optimization of large Lennard-Jones clusters

For improving the efficiency of dynamic lattice searching (DLS) method for unbiased optimization of large Lennard-Jones (LJ) clusters, a variant of the interior operation (IO) proposed by Takeuchi was combined with DLS. The method is named as DLS-IO. In the method, the IO moves outer atoms with higher energy toward the coordinates center, i.e., (0, 0, 0), of a cluster and a local minimization (LM) follows each IO. This makes the interior atoms more compact and the outer atoms more uniformly distributed with lower potential energy. Therefore, the starting structure for DLS operations is closer to the global optimum compared with the randomly generated structures. On the other hand, a method to identify the central atom is proposed for the central vacancy problem. Optimizations of LJ(500), LJ(561), LJ(660), LJ(665), and LJ(670) were investigated with the DLS-IO, and the structural transition during the optimization was analyzed. It was found that the method is efficient and unbiased for optimization of large LJ clusters, and it may be a promising approach to be universally used for structural optimizations.     

A dynamic lattice searching method for fast optimization of Lennard-Jones clusters

A highly efficient unbiased global optimization method called dynamic lattice searching (DLS) was proposed. The method starts with a randomly generated local minimum, and finds better solution by a circulation of construction and searching of the dynamic lattice (DL) until the better solution approaches the best solution. The DL is constructed adaptively based on the starting local minimum by searching the possible location sites for an added atom, and the DL searching is implemented by iteratively moving the atom located at the occupied lattice site with the highest energy to the vacant lattice site with the lowest energy. Because the DL can greatly reduce the searching space and the number of the time-consuming local minimization procedures, the proposed DLS method runs at a very high efficiency, especially for the clusters of larger size. The performance of the DLS is investigated in the optimization of Lennard-Jones (LJ) clusters up to 309 atoms, and the structure of the LJ(500) is also predicted. Furthermore, the idea of dynamic lattice can be easily adopted in the optimization of other molecular or atomic clusters. It may be a promising approach to be universally used for structural optimizations in the chemistry field.     

A dynamic lattice searching method with constructed core for optimization of large Lennard-Jones clusters

A variation of the previous dynamic lattice searching (DLS) method, named as DLS with constructed core (DLSc), was proposed for structural optimization of Lennard-Jones (LJ) clusters. In the new method, the starting random structure is generated with an icosahedron or a decahedron as a core. For a cluster with n shells, the atoms in the inner n - 2 shells are set as a fixed core, and the remaining atoms in the outer 2 shells are optimized by DLS. With applications of DLSc to optimization of LJ100-200 and LJ660-670, it was found that all the putative global minima can be obtained by using the DLSc method, and the method was proved to be high efficient compared with the previous DLS, because the searching space is reduced by the use of the fixed core. However, although DLSc is still an unbiased approach for smaller LJ clusters, it turned out to be biased for large ones. Further works are still needed to make it to be a more general method for cluster optimization problem.     

An unbiased population-based search for the geometry optimization of Lennard-Jones clusters: 2 < or = N < or = 372

This article presents the results obtained using an unbiased Population Based Search (PBS) for optimizing Lennard-Jones clusters. PBS is able to repeatedly obtain all putative global minima, for Lennard-Jones clusters in the range 2 < or = N < or = 372, as reported in the Cambridge Cluster Database. The PBS algorithm incorporates and extends key techniques that have been developed in other Lennard-Jones optimization algorithms over the last decade. Of particular importance are the use of cut-and-paste operators, structure niching (using the cluster strain energy as a metric), two-phase local search, and a new operator, Directed Optimization, which extends the previous concept of directed mutation. In addition, PBS is able to operate in a parallel mode for optimizing larger clusters.     

Composition-induced structural transitions in mixed Lennard-Jones clusters: global reparametrization and optimization

As extended benchmarks to global cluster structure optimization methods, we provide a first systematic point of entry into the world of strongly mixed rare gas clusters. A new set of generalized Lennard-Jones pair potentials is generated for this purpose, by fitting them to high-end ab initio reference data. Employing these potentials in our genetic algorithm-based global structure optimization framework, we examined various systems from binary to quinary mixtures of atom types. A central result from this study is that the famous fcc structure for 38 atoms can survive for certain binary mixtures but appears to be prone to collapsing into the dominating icosahedral structure, which we observed upon introduction of one single atom of a ternary type.     

Comparing search strategies for finding global optima on energy landscapes

We provide some tests of the convex global underestimator (CGU) algorithm, which aims to find global minima on funnel-shaped energy landscapes. We use two different potential functions—the reduced Lennard–Jones cluster potential, and the modified Sun protein folding potential, to compare the CGU algorithm with the simplest versions of the traditional trajectory-based search methods, simulated annealing (SA), and Monte Carlo (MC). For both potentials, the CGU reaches energies lower on the landscapes than both SA and MC, even when SA and MC are given the same number of starting points as in a full CGU run or when all methods are given the same amount of computer time. The CGU consistently finds the global minima of the Lennard–Jones potential for all cases with up to at least n=30 degrees of freedom. Finding the global or near-global minimum in the CGU method requires polynomial time [scaling between O(n³) and O(n⁴)], on average. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 1527–1532, 1999     

Generation and characterization of low‐energy structures in atomic clusters

Factors relevant for controlling the structures determined in the local optimization of argon clusters are investigated. In particular, the role of volume and shape for the box where initial structures are generated is assessed. A thorough characterization of the optimization is also presented, based on a nearest‐neighbor analysis, in clusters ranging from 30 to 55 atoms. This includes the assessment of the degree of preservation of aspects of the initial randomly generated structure in the final optimized counterpart, and the correlation between optimized energy and the number of nearest neighbors and average departure from the diatomic reference distance. The usefulness of this analysis to explore the energy landscape of atomic clusters is also highlighted. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

A machine learning technique toward generating minimum energy structures of small boron clusters

The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. Particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing, PSO, and so on, PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm‐intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.