Interpretation of butyltin mass spectra using isotope pattern reconstruction for the accurate measurement of isotope ratios from molecular clusters

The fragmentation patterns of butyltin compounds (mono-, di-, and tributyltin) in an electron impact ion source were studied using an isotope pattern reconstruction algorithm with emphasis on isotope ratio measurements from molecular clusters. For this purpose, standards of natural tin isotope abundance and a (119)Sn-enriched mixture of the three compounds were both ethylated and propylated using sodium tetraalkylborates. The corresponding mass spectra of the various tetraalkyltin compounds prepared were obtained by GC/MS after their extraction with hexane.The results showed that pure interference-free molecular clusters were obtained only for certain R(3)Sn(+) ions where no isobaric overlap with R(2)SnH(+) ions occurred (e.g. BuEt(2)Sn(+) overlaps with Bu(2)SnH(+)). These ions are ideal candidates for accurate Sn isotope ratio measurements, while isotope pattern perturbing interferences are observed for other molecular fragments down to Sn(.)(+). Isotope pattern reconstruction algorithm thus can be used as an analytical tool to ensure the absence of molecular interferences--a requirement for accurate isotope ratio measurements from molecular clusters. The relevance of these studies for the determination of butyltin compounds in environmental samples by isotope dilution GC/MS is also discussed.     

PDECO: Parallel differential evolution for clusters optimization

The optimization of the atomic and molecular clusters with a large number of atoms is a very challenging topic. This article proposes a parallel differential evolution (DE) optimization scheme for large‐scale clusters. It combines a modified DE algorithm with improved genetic operators and a parallel strategy with a migration operator to address the problems of numerous local optima and large computational demanding. Results of Lennard–Jones (LJ) clusters and Gupta‐potential Co clusters show the performance of the algorithm surpasses those in previous researches in terms of successful rate, convergent speed, and global searching ability. The overall performance for large or challenging LJ clusters is enhanced significantly. The average number of local minimizations per hit of the global minima for Co clusters is only about 3–4% of that in previous methods. Some global optima for Co are also updated. We then apply the algorithm to optimize the Pt clusters with Gupta potential from the size 3 to 130 and analyze their electronic properties by density functional theory calculation. The clusters with 13, 38, 54, 75, 108, and 125 atoms are extremely stable and can be taken as the magic numbers for Pt systems. It is interesting that the more stable structures, especially magic‐number ones, tend to have a larger energy gap between the highest occupied molecular orbital and the lowest unoccupied molecular orbital. It is also found that the clusters are gradually close to the metal bulk from the size N > 80 and Pt₃₈ is expected to be more active than Pt₇₅ in catalytic reaction. © 2013 Wiley Periodicals, Inc.     

Microsolvation of cationic dimers in 4He droplets: geometries of A+2(He)N (A=Li, Na, K) from optimized energies

Ab initio computed interaction forces are employed to describe the microsolvation of the A+2(2Sigma) (A=Li, Na, K) molecular ion in 4He clusters of small variable size. The minimum energy structures are obtained by performing energy minimization based on a genetic algorithm approach. The symmetry features of the collocation of solvent adatoms around the dimeric cation are analyzed in detail, showing that the selective growth of small clusters around the two sides of the ion during the solvation process is a feature common to all three dopants.     

Density matrix renormalization group calculations on relative energies of transition metal complexes and clusters

The accurate first-principles calculation of relative energies of transition metal complexes and clusters is still one of the great challenges for quantum chemistry. Dense lying electronic states and near degeneracies make accurate predictions difficult, and multireference methods with large active spaces are required. Often density functional theory calculations are employed for feasibility reasons, but their actual accuracy for a given system is usually difficult to assess (also because accurate ab initio reference data are lacking). In this work we study the performance of the density matrix renormalization group algorithm for the prediction of relative energies of transition metal complexes and clusters of different spin and molecular structure. In particular, the focus is on the relative energetical order of electronic states of different spin for mononuclear complexes and on the relative energy of different isomers of dinuclear oxo-bridged copper clusters.     

An efficient linear scaling procedure for constructing localized orbitals of large molecules based on the one-particle density matrix

We have developed a linear-scaling algorithm for obtaining the Boys localized molecular orbitals from the one-particle density matrix. The algorithm is made up of two steps: the Cholesky decomposition of the density matrix to obtain Cholesky molecular orbitals and the subsequent Boys localization process. Linear-scaling algorithms have been proposed to achieve linear-scaling calculations of these two steps, based on the sparse matrix technique and the locality of the Cholesky molecular orbitals. The present algorithm has been applied to compute the Boys localized orbitals in a number of systems including α-helix peptides, water clusters, and protein molecules. Illustrative calculations demonstrate that the computational time of obtaining Boys localized orbitals with the present algorithm is asymptotically linear with increasing the system size.     

Larger water clusters with edges and corners on their way to ice: structural trends elucidated with an improved parallel evolutionary algorithm

For the difficult task of finding global minimum energy structures for molecular clusters of nontrivial size, we present a highly efficient parallel implementation of an evolutionary algorithm. By completely abandoning the traditional concept of generations and by replacing it with a less rigid pool concept, we have managed to eliminate serial bottlenecks completely and can operate the algorithm efficiently on an arbitrary number of parallel processes. Nevertheless, our new algorithm still realizes all of the main features of our old, successful implementation. First tests of the new algorithm are shown for the highly demanding problem of water clusters modeled by a potential with flexible, polarizable monomers (TTM2-F). For this problem, our new algorithm not only reproduces all of the global minima proposed previously in considerably less CPU time but also leads to improved proposals in several cases. These, in turn, qualitatively change our earlier predictions concerning the transitions from all-surface structures to cages with a single interior molecule, and from one to two interior molecules. Furthermore, we compare preliminary results up to n = 105 with locally optimized cuts from several ice modifications. This comparison indicates that relaxed ice structures may start to be competitive already at cluster sizes above n = 90.     

Quantum Spin Dynamics in Molecular Magnets

Summary.  The detailed theoretical understanding of quantum spin dynamics in various molecular magnets is an important step on the roadway to technological applications of these systems. Quantum effects in both ferromagnetic and antiferromagnetic molecular clusters are, by now, theoretically well understood. Ferromagnetic molecular clusters allow one to study the interplay of incoherent quantum tunneling and thermally activated transitions between states with different spin orientation. The Berry phase oscillations found in Fe₈ are signatures of the quantum mechanical interference of different tunneling paths. Antiferromagnetic molecular clusters are promising candidates for the observation of coherent quantum tunneling on the mesoscopic scale. Although challenging, application of molecular magnetic clusters for data storage and quantum data processing are within experimental reach already with present day technology. Corresponding author. E-mail: Daniel.Loss@unibas.ch Received May 7, 2002; accepted May 22, 2002     

Combining genetic algorithm and simulated annealing: a molecular geometry optimization study

We introduce a new hybrid approach to determine the ground state geometry of molecular systems. Firstly, we compared the ability of genetic algorithm (GA) and simulated annealing (SA) to find the lowest energy geometry of silicon clusters with six and 10 atoms. This comparison showed that GA exhibits fast initial convergence, but its performance deteriorates as it approaches the desired global extreme. Interestingly, SA showed a complementary convergence pattern, in addition to high accuracy. Our new procedure combines selected features from GA and SA to achieve weak dependence on initial parameters, parallel search strategy, fast convergence and high accuracy. This hybrid algorithm outperforms GA and SA by one order of magnitude for small silicon clusters (Si₆ and Si₁₀). Next, we applied the hybrid method to study the geometry of a 20-atom silicon cluster. It was able to find an original geometry, apparently lower in energy than those previously described in literature. In principle, our procedure can be applied successfully to any molecular system.     

Parametric effects of the potential energy function on the geometrical features of ternary lennard-jones clusters

The impact of parameters in potential function for describing atomic or molecular clusters is complex due to the complicated potential energy surface. Ternary Lennard-Jones (TLJ) A(l)B(m)C(n) clusters with two-body potential are investigated to study the effect of parameters. In the potential, the size parameter (σ(AA)) of A atoms is fixed, and corresponding parameters of B and C atoms (relative to A atoms), i.e., σ(BB)/σ(AA) and σ(CC)/σ(AA) > 1.00, are used to control the atomic interaction among A, B, and C atoms in TLJ clusters. The minimum energy configurations of A(l)B(m)C(n) clusters with different species are optimized by adaptive immune optimization algorithm. Ternary cluster structures, bonds, and energies of the putative minima are studied. The results show that two different structures based on double-icosahedra are found in 30-atom TLJ clusters. Furthermore, with increasing potential size parameters of B and C atoms, A atoms tend to be more compact for the increasing numbers of A-A bonds, but the short-range attractive part in TLJ clusters becomes weaker. To lower the potential energy, B and C atoms grow around the A atoms in pursuit of a compact configuration. The results are also approved in A(l)B(m)C(n) (l + m + n = 9-55) clusters and A(l)B(m)C(n) (l = 13, m + n = 42) clusters.     

Kick: constraining a stochastic search procedure with molecular fragments

An extension of the Kick program developed by Bera et al. (J Phys Chem A 2006, 110, 4287) is described in which chemically sensible molecular fragments are used in an automated stochastic search algorithm. This results in a vastly reduced region of the potential energy surface which can be explored very quickly. We present use of this modified algorithm to the search for low-lying isomers, and we present candidates for the global energy minimum, for a range of chemical systems. We highlight the usefulness of this procedure for exploring reactions of molecules with transition metal clusters and to the microsolvation of a small dipeptide.     

Microsolvation of an ionic dopant in small (4)He clusters: OH(+)((3)sigma)((4)He)(N) via genetic algorithm optimizations.

The optimized spatial structures of the small clusters (with N up to 33) formed by an increasing number of (4)He atoms, which act as a microsolvent surrounding the OH(+) ionic molecular dopant, are obtained using a sum-of-potentials scheme corrected by three-body (3B) effects. The most stable structures are generated using the type of genetic algorithm described herein, and the sequential formation of regular shell structures is analyzed in detail. Possible quantum corrections for both the solvent distributions and the stable energetics are analyzed and discussed.     

Software to obtain spatially localized functions from different radial functions

We have developed an algorithm that enables simplified box orbital functions (SBO) to be obtained with optimized coefficients by fitting them to functions of many types. SBOs are a linear combination of radial functions useful in quantum chemistry calculations which can be spatially restricted (defined in $$0 \le r < r_{0}$$ interval, and zero for $$r \ge r_{0}$$). The algorithm proposed makes it possible to obtain the optimal radius $$r_{0}$$ and the coefficients of the SBOs of any number of terms from the functions to be fitted, but also allows the user to define a particular radius r and calculate the coefficients of the combination of terms of the SBOs. SBOs have proved to be useful in the calculation of molecular properties, and can reduce the complexity of the integral calculations, especially in huge chemical systems such as atomic clusters. These types of functions are also adequate for studying confined systems such as molecules in solution or big chemical systems such as atomic clusters. In addition, while carrying out the examples presented in this study we have tested the suitability of SBO functions to calculate molecular reactivity, showing that the basis functions provide results as good as the basis sets typically used for this kind of calculations.     

Chemistry of tantalum clusters in solution and on SiO2 supports: analogies and contrasts

Tantalum clusters have been synthesized from Ta(CH2Ph)5 on the surface of porous fumed SiO2. When these clusters are small, incorporating, on average, several Ta atoms, their chemistry is similar to that of molecular tantalum clusters (and other early transition-metal) clusters. For example, The Ta-Ta bonds in these small supported clusters have been characterized by extended X-ray absorption fine structure (EXAFS), IR, and UV-vis spectroscopy, being similar to those in molecular analogues. The redox reactions of the supported clusters, characterized by X-ray absorption near-edge structure, are analogous to those of early transition-metal clusters in solution. In contrast to the largest of these clusters in solution and in the solid state, those supported on SiO2 are raftlike, facilitating the substantial metal-support-oxygen bonding that is evident in the EXAFS spectra. Samples consisting of tantalum clusters on SiO2 catalyze alkane disproportionation and the conversion of methane with n-butane to give other alkanes, but catalytic properties of analogous clusters in solution have barely been explored.     

Multi-swarm UPSO algorithm based on seed strategy for atomic clusters structure optimization

Particle Swarm Optimization (PSO) algorithm is prone to get trapped in local optima and insufficient information exchange among particles. To solve this problem, this paper proposes a Multi-swarm Unified Particle Swarm Optimization algorithm based on Seed Strategy (SS-DMS-UPSO) to optimize the atomic clusters structure. In this algorithm, the population is divided into some sub-populations evolving randomly and evenly, and each sub-population uses UPSO algorithm with different unification factors to evolve independently in parallel. After a certain number of independent evolution, the particles of all sub-populations are merged into a new population, and the population is again randomly divided into average sub-populations. Iterate the algorithm repeatedly in this way. And finally the global best particle can be obtained. The experimental results show that the SS-DMS-UPSO algorithm can search for the optimal structure or extremely similar optimal structure for atomic clusters with atomic numbers between 2 and 31. For atomic clusters with atomic numbers between 32 and 35, the algorithm can find its approximate optimal structure. Compared with other algorithms, the difference between the lowest energy value and the ideal energy value obtained by the SS-DMS-UPSO algorithm is much smaller. It means that its optimal structure of the atomic clusters is closer to the stable structure, and the algorithm is more stable, which proves the effectiveness of the SS-DMS-UPSO algorithm.     

Similarity recognition of molecular structures by optimal atomic matching and rotational superposition

An algorithm for similarity recognition of molecules and molecular clusters is presented which also establishes the optimum matching among atoms of different structures. In the first step of the algorithm, a set of molecules are coarsely superimposed by transforming them into a common reference coordinate system. The optimum atomic matching among structures is then found with the help of the Hungarian algorithm. For this, pairs of structures are represented as complete bipartite graphs with a weight function that uses intermolecular atomic distances. In the final step, a rotational superposition method is applied using the optimum atomic matching found. This yields the minimum root mean square deviation of intermolecular atomic distances with respect to arbitrary rotation and translation of the molecules. Combined with an effective similarity prescreening method, our algorithm shows robustness and an effective quadratic scaling of computational time with the number of atoms.     

Cage Clusters: from Structure Prediction to Rational Design of Functional Nanomaterials

Atomic clusters of subnanometer scale and variable chemical composition offer great opportunities for rational design of functional nanomaterials. Among them, cage clusters doped with endohedral atom are particularly interesting owing to their enhanced stability and highly tunable physical and chemical properties. In this perspective, first we give a brief overview of the history of doped cage clusters and introduce the home-developed comprehensive genetic algorithm(CGA) for structure prediction of clusters. Then, we show a few examples of magnetic clusters and subnanometer catalysts based on doped cage clusters, which are computationally revealed or designed by the CGA code. Finally, we give an outlook for some future directions of cluster science.     

Global optimization of clusters using electronic structure methods

Over the past decade, there has been a significant growth in the development and application of methods for performing global optimization (GO) of cluster and nanoparticle structures using first‐principles electronic structure methods coupled to sophisticated search algorithms. This has in part been driven by the desire to avoid the use of empirical potentials (EPs), especially in cases where no reliable potentials exist to guide the search toward reasonable regions of configuration space. This has been facilitated by improvements in the reliability of the search algorithms, increased efficiency of the electronic structure methods, and the development of faster, multiprocessor high‐performance computing architectures. In this review, we give a brief overview of GO algorithms, though concentrating mainly on genetic algorithm and basin hopping techniques, first in combination with EPs. The major part of the review then deals with details of the implementation and application of these search methods to allow exploration for global minimum cluster structures directly using electronic structure methods and, in particular, density functional theory. Example applications are presented, ranging from isolated monometallic and bimetallic clusters to molecular clusters and ligated and surface supported metal clusters. Finally, some possible future developments are highlighted. © 2013 Wiley Periodicals, Inc.     

An adaptive immune optimization algorithm for energy minimization problems

Based on the immune theory of biology, a novel evolutionary algorithm, adaptive immune optimization algorithm (AIOA), is proposed. In AIOA, density regulation and immune selection is adopted to control the individual diversity and the convergence adaptively. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm is a highly efficient optimization method compared with other stochastic optimization methods. The algorithm was also applied to the optimization of Lennard-Jones clusters, and the results show that the method can find the optimal structure of N相似文献