A study of genetic algorithm approaches to global geometry optimization of aromatic hydrocarbon microclusters

Prediction of octanol-air partition coefficients for PCBs at different ambient temperatures based on the solvation free energy and the dimer ratio

Temperature-dependent octanol-air partition coefficients (K_OA) are of great importance in assessing the environmental behavior and fate of persistent organic pollutants including polychlorinated biphenyls (PCBs). Due to the tremendous amounts of time, effort and cost needed for the experimental determination of K_OA, it is desirable to develop a rapid and precise predictive method to estimate K_OA just based on molecular structure. In the present study, a predictive model for log K_OA of PCBs at ambient temperatures was developed based on the thermodynamic relationship between K_OA and the solvation free energy from air to octanol (ΔG_OA). For the calculation of ΔG_OA of PCBs, the optimal combination of theoretical method and basis-set was identified to be HF/MIDI!6D for both geometry optimization and energy calculation. Dimer formation could affect the partition behavior and promote the apparent K_OA values of PCBs. After taking the effect of dimer formation into account, the goodness-of-fit, predictive ability, and robustness of the predictive model were significantly improved. Apparent log K_OA values of PCBs at different ambient temperatures ranging from 283.15 to 303.15 K were predicted. Compared with other reported models, the model developed in the present study had not only comparable goodness-of-fit and predictive ability, but also a universal application domain and the relative independency of experimental data. Therefore, the solvation free energy method could be a promising method for the prediction of K_OA.

Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc.

Exploring energy landscapes: from molecular to mesoscopic systems

We review a comprehensive computational framework to survey the potential energy landscape for systems composed of rigid or partially rigid molecules. Illustrative case studies relevant to a wide range of molecular clusters and soft and condensed matter systems are discussed.

A survey of the potential energy surface for the (benzene)13 cluster

We report on a survey of the potential energy surface for the 13-molecule benzene cluster, (C(6)H(6))(13), bound by an atom-atom intermolecular potential developed from first principles. The potential, which has an anisotropic repulsion term, is found to support distinct pairs of structures of C(3), C(i), and S(6) symmetry as low-lying minima, including a C(3) global minimum. The organisation of the low-lying region of the potential energy surface suggests that one of the S(6) structures is likely to act as a kinetic trap, hindering efficient relaxation to the global minimum, in agreement with the hypothesis that two isomers coexist in this system, suggested by spectroscopic experiments.

Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field

This article describes the application of our distributed computing framework for crystal structure prediction (CSP) the modified genetic algorithms for crystal and cluster prediction (MGAC), to predict the crystal structure of flexible molecules using the general Amber force field (GAFF) and the CHARMM program. The MGAC distributed computing framework includes a series of tightly integrated computer programs for generating the molecule's force field, sampling crystal structures using a distributed parallel genetic algorithm and local energy minimization of the structures followed by the classifying, sorting, and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential.

Global minimum structure searches via particle swarm optimization

Novel implementation of the evolutionary approach known as particle swarm optimization (PSO) capable of finding the global minimum of the potential energy surface of atomic assemblies is reported. This is the first time the PSO technique has been used to perform global optimization of minimum structure search for chemical systems. Significant improvements have been introduced to the original PSO algorithm to increase its efficiency and reliability and adapt it to chemical systems. The developed software has successfully found the lowest-energy structures of the LJ(26) Lennard-Jones cluster, anionic silicon hydride Si(2)H(5) (-), and triply hydrated hydroxide ion OH(-) (H(2)O)(3). It requires relatively small population sizes and demonstrates fast convergence. Efficiency of PSO has been compared with simulated annealing, and the gradient embedded genetic algorithm.

Novel Method for Geometry Optimization of Molecular Clusters: Application to Benzene Clusters

A heuristic and unbiased method for searching optimal geometries of clusters of nonspherical molecules was constructed from the algorithm recently proposed for Lennard-Jones atomic clusters. In the method, global minima are searched by using three operators, interior, surface, and orientation operators. The first operator gives a perturbation on a cluster configuration by moving molecules near the center of mass of a cluster, and the second one modifies a cluster configuration by moving molecules to the most stable positions on the surface of a cluster. The moved molecules are selected by employing a contribution of the molecules to the potential energy of a cluster. The third operator randomly changes the orientations of all molecules. The proposed method was applied to benzene clusters. It was possible to find new global minima for (C6H6)11, (C6H6)14, and (C6H6)15. Global minima for (C6H6)16 to (C6H6)30 are first reported in this article.

Structure of the Na(x)Cl(x+1) (-) (x=1-4) clusters via ab initio genetic algorithm and photoelectron spectroscopy

The application of the ab initio genetic algorithm with an embedded gradient has been carried out for the elucidation of global minimum structures of a series of anionic sodium chloride clusters, Na(x)Cl(x+1) (-) (x=1-4), produced in the gas phase using electrospray ionization and studied by photoelectron spectroscopy. These are all superhalogen species with extremely high electron binding energies. The vertical electron detachment energies for Na(x)Cl(x+1) (-) were measured to be 5.6, 6.46, 6.3, and 7.0 eV, for x=1-4, respectively. Our ab initio gradient embedded genetic algorithm program detected the linear global minima for NaCl(2) (-) and Na(2)Cl(3) (-) and three-dimensional structures for the larger species. Na(3)Cl(4) (-) was found to have C(3v) symmetry, which can be viewed as a Na(4)Cl(4) cube missing a corner Na(+) cation, whereas Na(4)Cl(5) (-) was found to have C(4v) symmetry, close to a 3x3 planar structure. Excellent agreement between the theoretically calculated and the experimental spectra was observed, confirming the obtained structures and demonstrating the power of the developed genetic algorithm technique.