Non-genetic global optimization methods in molecular science: An overview

Calvo, F.

doi:10.1016/j.commatsci.2008.03.059

Cited by 18 publications

(7 citation statements)

References 64 publications

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“…At zero Kelvin, and dispensing with nuclear quantum effects, the cluster adopts a single solid structure that minimizes the cluster potential energy. Locating this global minimum (GM) structure is a complex optimization problem for which a variety of algorithms, such as genetic (76) or basin hopping (77), have been developed and reviewed (78). The melting transition usually is studied by modeling the heating of the GM structure, either with molecular dynamics (MD) or Monte Carlo techniques (79).…”

Section: Theoretical Methodsmentioning

confidence: 99%

Melting and Freezing of Metal Clusters

Aguado

Jarrold

2011

Annu. Rev. Phys. Chem.

109

108

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Recent developments allow heat capacities to be measured for size-selected clusters isolated in the gas phase. For clusters with tens to hundreds of atoms, the heat capacities determined as a function of temperature usually have a single peak attributed to a melting transition. The melting temperatures and latent heats show large size-dependent fluctuations. In some cases, the melting temperatures change by hundreds of degrees with the addition of a single atom. Theory has played a critical role in understanding the origin of the size-dependent fluctuations, and in understanding the properties of the liquid-like and solid-like states. In some cases, the heat capacities have extra features (an additional peak or a dip) that reveal a more complex behavior than simple melting. In this article we provide a description of the methods used to measure the heat capacities and provide an overview of the experimental and theoretical results obtained for sodium and aluminum clusters.

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Section: Theoretical Methodsmentioning

confidence: 99%

Melting and Freezing of Metal Clusters

Aguado

Jarrold

2011

Annu. Rev. Phys. Chem.

109

108

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“…44,45 Briefly, the method is similar to the Monte Carlo + minimization scheme of Li and Scheraga 46 and consists of randomly moving all atoms in the system and minimizing the potential energy of the structure by a standard procedure such as conjugate gradient. The new geometry is accepted based on a Metropolis criterion, and upon acceptance serves as a new starting point for the next Monte Carlo move.…”

Section: B Global Optimizationmentioning

confidence: 99%

Many-body effects on the structures and stability of Ba2+Xen (n = 1–39, 54) clusters

Abdessalem

Habli

Ghalla

et al. 2014

The Journal of Chemical Physics

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The structures and relative stabilities of mixed Ba(2+)Xe(n) (n = 1-39, 54) clusters have been theoretically studied using basin-hopping global optimization. Analytical potential energy surfaces were constructed from ab initio or experimental data, assuming either purely additive interactions or including many-body polarization effects and the mutual contribution of self-consistent induced dipoles. For both models the stable structures are characterized by the barium cation being coated by a shell of xenon atoms, as expected from simple energetic arguments. Icosahedral packing is dominantly found, the exceptional stability of the icosahedral motif at n = 12 being further manifested at the size n = 32 where the basic icosahedron is surrounded by a dodecahedral cage, and at n = 54 where the transition to multilayer Mackay icosahedra has occurred. Interactions between induced dipoles generally tend to decrease the Xe-Xe binding, leading to different solvation patterns at small sizes but also favoring polyicosahedral growth. Besides attenuating relative energetic stability, many-body effects affect the structures by expanding the clusters by a few percents and allowing them to deform more.

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“…The explicit consideration of water molecules surrounding the nucleic acid bases (NABs) leads to clusters for which the physically reasonable structures cannot be guessed easily. While the approach consisting of generating such structures “by hand” becomes less and less efficient when the number of water molecules increases, the location of the proper nuclear configurations, which corresponds to a particular set of minima within a potential energy surface (PES), can be performed by the use of global search algorithms …”

Section: Introductionmentioning

confidence: 99%

The Use of the GSAM Approach for the Structural Investigation of Low-Lying Isomers of Molecular Clusters from Density-Functional-Theory-Based Potential Energy Surfaces: The Structures of Microhydrated Nucleic Acid Bases

2013

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This study presents structural properties of microhydrated nucleic acid bases (NABs) - uracil (U), thymine (T), guanine (G), adenine (A), and cytosine (C) - investigated by theoretical computations at the B3LYP level of theory. To obtain the different representations of these microhydrated species, we applied the GSAM procedure: the most stable conformers labeled X,nH2O (X = U, T, G, A and n = 1...5) for which the Boltzmann population is higher than 2% at 298K are calculated at the B3LYP and B3LYP-D levels of theory. At the B3LYP level, our calculated geometries are compared with those obtained in the literature. New physically relevant isomers are found with the GSAM algorithm, especially for the tetra- and pentahydrated species. The use of DFT-D functional does not strongly modify the relative energies of the isomers for the monohydrated species. When the number of water molecules increases, the results become extremely sensitive to the consideration of dispersion contributions.

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Non-genetic global optimization methods in molecular science: An overview

Cited by 18 publications

References 64 publications

Melting and Freezing of Metal Clusters

Melting and Freezing of Metal Clusters

Many-body effects on the structures and stability of Ba2+Xen (n = 1–39, 54) clusters

The Use of the GSAM Approach for the Structural Investigation of Low-Lying Isomers of Molecular Clusters from Density-Functional-Theory-Based Potential Energy Surfaces: The Structures of Microhydrated Nucleic Acid Bases

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