Exploring the MP2 energy surface of nanoalloy clusters with a genetic algorithm: Application to sodium–potassium

Silva, F.T.; Galvão, Breno R. L.; Voga, Geison P.; Silva, M.X.; Rodrigues, Domingos da Costa; Belchior, Jadson C.

doi:10.1016/j.cplett.2015.09.016

Cited by 15 publications

(21 citation statements)

References 39 publications

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“…It is possible to perform the global optimization search directly at the ab initio or density functional theory (DFT), but this has the disadvantage of being very time‐consuming. One feasible alternative is to construct an analytical potential energy surface (PES) (either by establishing a theoretically based function or using a neural network approach), which is then used by the global optimization method to discover a relevant set of low‐energy structures; another tested approach for generating a promising pool of structures was also proposed in References , by coupling standard optimization algorithms with semi‐empirical Hamiltonians or the Hartree‐Fock method.…”

Section: Introductionmentioning

confidence: 99%

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases

Jesus

Marques

Prudente

et al. 2018

Int J of Quantum Chemistry

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An evolutionary algorithm was used to search for the low-energy structures of Li + Ar n and Li + Kr n (n = 1 − 14). Two functions were used to describe the interaction potential at the CCSD(T)/augcc-pVQZ level of theory: one is based on a sum of all pair potentials, whereas the other includes three-body interactions. In general, the global minimum structures are similar for both Li + Ar n and Li + Kr n . Modifications in the octahedral structure of the first solvation shell lead to a high-energy penalty. Conversely, the second solvation shell shows a panoply of minima with similar energies that are likely to be interconverted. Post-optimization at the MP2 level confirmed that, for n = 2 and 3, one has to include three-body terms in the potential to reproduce the low-energy structures. Additionally, MP2 calculations indicate that energy reorder of the global minimum structure observed for Li + Kr 8 is related to the Kr 3 Axilrod-Teller-Muto term included in the potential.ab initio calculations, clusters, evolutionary algorithm, geometry optimization, microsolvation | INTRODUCTIONSolvation is a complex phenomenon with great relevance for the broad area of chemical physics. It is intimately related with the interactions that can be established among solute and solvent molecules, although the contribution from entropic effects can only be correctly described when incorporating all degrees of freedom of the system. Two main theoretical approaches are followed in the study of solvation: (1) continuum solvation models that do not treat solvent molecules as individual entities; and (2) explicit microsolvation methodology, where the solvent molecules are successively added to the system. Although the continuum models are able to incorporate the polarization of the solvent, they cannot explicitly describe interactions with a strong directional character and, in this way, environments comprising several solvents, ionic solutes, and biological systems are difficult to be accounted for. To somehow reduce these drawbacks, continuum-solvent methods [1] have been improved to approximately incorporate the structure of the solvent, [2] as well as the formation of the cavity for solute and the dispersion interaction by Abbreviations: ATM, Axilrod-Teller-Muto; aug-cc-pVQZ, augmented correlation consistent with polarized-valence quadruple-zeta basis set.; BSSE, basis set superposition error; cc-pVQZ, correlation consistent with polarized-valence quadruple-zeta basis set; CCSD(T), couple cluster with singles and doubles and perturbated triples excitations; EA, evolutionary algorithm; MP2, second-order Moller-Plesset.

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Section: Introductionmentioning

confidence: 99%

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases

Jesus

Marques

Prudente

et al. 2018

Int J of Quantum Chemistry

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“…σx- indicates the standard error and N fails indicates the relative number of times the global minimum was not reached .* The unconverged runs were removed from the calculation of these averages. This removal may compromise the analysis when N fails is nonzero .** Previous work Silva et al (2015) .…”

Section: Resultsmentioning

confidence: 99%

“…Still for the LJ 26 case, the sphere-cut-splice crossover (SCCR) performed poorly, which was expected since Chen et al indeed reported that this operator is more suitable for larger clusters (Chen et al, 2013). In our previous work (Silva et al, 2015), the employed build (PREV) was mainly composed by SCCR, but also counted with the immigration operator and a different evolutionary scheme. Within the present GA approach, our PREV build presented worse performance (falseNLM^=720) than SCCR-only build (falseNLM^=371) when it comes to the average number of local minimizations needed to reach global minimum.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, AUTO13 build is composed by all the operators tested herein: TO, IO, PCCR, SAO, IMM, AO, GCDO, TWCR, CDO, SCCR, UNCR, ARCR, and DYM. We have also run the same build of our previous work, here named PREV, which is composed of 70% SCCR, 20% DYM, and 10% IMM, kept fixed throughout the GA execution (Silva et al, 2015). Operator acronyms were defined in section 3.5.…”

Section: Methodsmentioning

confidence: 99%

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A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies

Silva

Belchior

2019

Front. Chem.

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A new procedure is suggested to improve genetic algorithms for the prediction of structures of nanoparticles. The strategy focuses on managing the creation of new individuals by evaluating the efficiency of operators (o1, o2,…,o13) in generating well-adapted offspring. This is done by increasing the creation rate of operators with better performance and decreasing that rate for the ones which poorly fulfill the task of creating favorable new generation. Additionally, several strategies (thirteen at this level of approach) from different optimization techniques were implemented on the actual genetic algorithm. Trials were performed on the general case studies of 26 and 55-atom clusters with binding energy governed by a Lennard-Jones empirical potential with all individuals being created by each of the particular thirteen operators tested. A 18-atom carbon cluster and some polynitrogen systems were also studied within REBO potential and quantum approaches, respectively. Results show that our management strategy could avoid bad operators, keeping the overall method performance with great confidence. Moreover, amongst the operators taken from the literature and tested herein, the genetic algorithm was faster when the generation of new individuals was carried out by the twist operator, even when compared to commonly used operators such as Deaven and Ho cut-and-splice crossover. Operators typically designed for basin-hopping methodology also performed well on the proposed genetic algorithm scheme.

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“…Other recently developed first principles based GAs are, for example, OGOLEM [124], a versatile poolbased GA implementation for clusters and flexible molecules and a GA that uses machine-learning techniques to improve its performance [129]. Though the GA-DFT approach is the most widely used method for the GO with first principle methods, there is also a GA study which uses MP2 calculations for sodium potassium nanoalloy clusters [133]. The following briefly describes the development of the GA-DFT approach within the Birmingham Cluster Genetic Algorithm and its derivatives, developed by Johnston and collaborators.…”

Section: Genetic Algorithms For Optimizing Cluster Structuresmentioning

confidence: 99%

First principles global optimization of metal clusters and nanoalloys

Jäger

Johnston

2018

Advances in Physics: X

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The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has become a very important and sophisticated research field in modern nanoscience. The possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys.

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Exploring the MP2 energy surface of nanoalloy clusters with a genetic algorithm: Application to sodium–potassium

Cited by 15 publications

References 39 publications

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases

A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies

First principles global optimization of metal clusters and nanoalloys

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Exploring the MP2 energy surface of nanoalloy clusters with a genetic algorithm: Application to sodium–potassium

Cited by 15 publications

References 39 publications

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li+ by rare gases

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li+ by rare gases

A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies

First principles global optimization of metal clusters and nanoalloys

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Product

Resources

About

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases

Exploring the first‐shell and second‐shell structures arising in the microsolvation of Li⁺ by rare gases