Generation and characterization of low‐energy structures in atomic clusters

Marques, Jorge M. C.; Pais, Alberto A. C. C.; Abreu, Paulo E.

doi:10.1002/jcc.21436

Cited by 14 publications

(7 citation statements)

References 39 publications

(60 reference statements)

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“…In particular, we tested our procedure for the Morse potential, [18,19] which by means of a single parameter q determines the range of the interaction, and thus adds flexibility to the model. In particular, we tested our procedure for the Morse potential, [18,19] which by means of a single parameter q determines the range of the interaction, and thus adds flexibility to the model.…”

Section: Seeds and The Size Of The Initial Boxmentioning

confidence: 99%

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A strategy to find minimal energy nanocluster structures

et al. 2013

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An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard-Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ13 , 10 new local minima for LJ14 , and thousands of new local minima for 15≤N≤65. Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low-energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two- and three-dimensional, that can be used as an input for refinement by means of ab initio methods.

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Section: Seeds and The Size Of The Initial Boxmentioning

confidence: 99%

“…[18,19] Moreover, the procedure is not restricted to classical potentials, to which we limit ourselves for the time being, but we have obtained some preliminary results for small clusters using it in combination with ab initio calculations. However, the procedure is by no means restricted to LJ-type interactions between atoms.…”

Section: Introductionmentioning

confidence: 99%

A strategy to find minimal energy nanocluster structures

et al. 2013

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“…[15,16] and references therein). Clearly, EAs are among the best methods for global geometry optimization of clusters, and they are known to over perform random search algorithms, like the so-called big-bang method [17], as we have shown in a recent work [18,19].…”

Section: Introductionmentioning

confidence: 99%

New insights on lithium-cation microsolvation by solvents forming hydrogen-bonds: Water versus methanol

Llanio-Trujillo

Marques

Pereira

2013

Computational and Theoretical Chemistry

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This investigation uses a recent methodology, essentially based on our evolutionary algorithm (EA) to get new insights about the energetics and structure of the first solvation shells of lithium ion in polar solvents that form important hydrogen bonds. We employed the EA to search for the low-energy structures of the Li + (H 2 O) n and Li + (CH 3 OH) n clusters (with n 6 20) as modeled by commonly used rigid nonpolarizable force-field potentials. Particular emphasis is given to the characterization of the putative global minima; for Li + (H 2 O) 17 , the EA discovered a new global minimum with five water molecules directly coordinating the ion. Smaller-size clusters were, then, re-optimized by employing electronicstructure methods, namely, DFT (with the B3LYP functional and both the 6-31+G ⁄ and 6-311+G ⁄⁄ basis sets) and MP2 (with the aug-cc-pVDZ basis set). In the case of Li + (H 2 O) n , the ab initio global minimum structures are similar to those obtained with the EA up to n = 10. However, for n = 17, the structure of the global minimum discovered by the EA is different from the lowest-energy cluster obtained after re-optimization at the MP2/augcc-pVDZ level of theory. Such energy reorder may be attributed to the water-water interaction. As for the Li + (CH 3 OH) n clusters, the re-optimization process leads more often to a reorder in the energy of the minimum structures. Thus, forfluxional clusters like the Li + (CH 3 OH) n ones that show a huge number of stationary configurations within a small energy window, it is mandatory to carefully choose various structures, besides the global minimum, to be re-optimized at the ab initio or DFT levels. Due to the difficulty on choosing adequate departing structures by the usually employed chemical intuition, we noticed that some low-energy minima (including the global one) of even small Li + (CH 3 OH) n clusters were missed in literature. We showcase this problem in the Li + (CH 3 OH) 6 cluster, whose vibrational frequencies in the CO stretching region and corresponding infrared intensities were calculated at the DFT level of theory and compared with previously reported results.

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“…It is well-known that Ar 38 presents a double-funnel energy landscape, and hence, we can assign the global minimum to the deepest funnel and the remaining four structures to the other one (cf. ref ). Then, if this corresponds to a structural signature, it should be apparent in the rmsd values resulting from the best superposition of those structures.…”

Section: Superimposing Methodsmentioning

confidence: 99%

“…Five low-energy minimum structures of Ar 38 located in our previous work . They belong to the symmetry point groups (ordered from the lowest to the highest energy) (a) O h , (b) C 5 v , (c) C s , (d) C 5 v (denoted in the text as C 5 v ′), and (e) C 1 .…”

Section: Superimposing Methodsmentioning

confidence: 99%

How Different Are Two Chemical Structures?

Marques

Llanio-Trujillo

Abreu

et al. 2010

J. Chem. Inf. Model.

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We extend the scope of a recent method for superimposing two molecules ( J. Chem. Phys. 2009, 131, 124126-1-124126-10 ) to include the identification of chiral structures. This methodology is tested by applying it to several organic molecules and water clusters that were subjected to geometry optimization. The accuracy of four simpler, non-superimposing approaches is then analyzed by comparing pairs of structures for argon and water clusters. The structures considered in this work were obtained by a Markovian walk in the coordinate space. First, a random geometry is generated, and then, the iterative application of a mutation operator ensures the creation of increasingly dissimilar structures. The discriminating power of the non-superimposing approaches is tested by comparing the corresponding dissimilarity measures with the root-mean-square distance obtained from the superimposing method. Finally, we showcase the application of those methods to characterize the diversity of solutions in global geometry optimization by evolutionary algorithms.

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Generation and characterization of low‐energy structures in atomic clusters

Cited by 14 publications

References 39 publications

A strategy to find minimal energy nanocluster structures

A strategy to find minimal energy nanocluster structures

New insights on lithium-cation microsolvation by solvents forming hydrogen-bonds: Water versus methanol

How Different Are Two Chemical Structures?

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