Empirical-potential global minima and DFT local minima of trimetallic Ag Au Pt  (l+m+n= 13, 19, 33, 38) clusters

Pacheco-Contreras, Rafael; Juárez-Sánchez, J. Octavio; Dessens-Félix, Maribel; Aguilera‐Granja, F.; Fortunelli, Alessandro; Posada-Amarillas, Álvaro

doi:10.1016/j.commatsci.2017.09.022

Cited by 33 publications

(17 citation statements)

References 53 publications

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“…The mixing energy (Song et al, 2005; Pacheco-Contreras et al, 2018) is an indicator of the stability of the binary cluster with respect to its unary counterpart, given by Equation (5):…”

Section: Theoretical Approachmentioning

confidence: 99%

A Global Optimizer for Nanoclusters

2019

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We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an n-sized cluster is built from the geometries of n−1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to n using this evolutionary growth strategy. We have used our strategy on some of the well-studied clusters such as Pd, Pt, Au, and Al homometallic clusters, Ru-Pt and Au-Pt binary clusters, and Ag-Au-Pt ternary cluster. We have analyzed some of the popular parameters to characterize the clusters, such as relative energy, singlet-triplet energy difference, binding energy, second-order energy difference, and mixing energy, and compared with the reported properties.

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“…The mixing energy (Song et al, 2005; Pacheco-Contreras et al, 2018) is an indicator of the stability of the binary cluster with respect to its unary counterpart, given by Equation (5):…”

Section: Theoretical Approachmentioning

confidence: 99%

A Global Optimizer for Nanoclusters

2019

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“…There are several examples where first principle calculations and experimental findings disagree with EP computations regarding the cluster structure or relative energies of different isomers. For example, GO of trimetallic Ag k Au m Pt n (k + m + n = 13,19,33,38) clusters result in different GMs for EP and DFT calculations [92]. GO of Si n (n = 16-20) clusters employing three different EPs, as well as DFT computations, provides different GMs for each theory level [64].…”

Section: Global Optimization Methods For Metal Clusters and Nanoalloysmentioning

confidence: 99%

“…Clusters of noble metal elements are of considerable research interest, due to their intriguing optical properties and promising applications in catalysis and other technologies. Gold shows unique physical and chemical properties, which are significantly influenced by relativistic effects [11,29,88,90,92,96,103,107,109,111]. There have been several GO studies on gold clusters using EPs followed by DFT relaxation [164][165][166].…”

Section: Au Clustersmentioning

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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“…However, crossinteraction parameters are sometimes difficult to optimize due to the lack of experimental information, e.g., phase diagrams, and several combining rules have been developed, for example for Lennard-Jones systems [28]. Thus, a strategy for obtaining Gupta potential heteroatomic parameters, by taking arithmetic average values of the homoatomic interactions, has been widely explored by some of the authors of this work, with reasonable results [29][30][31][32][33]. Ptcore-Cushell copper-rich cluster was singled out due to the enhanced catalytic activity observed in Cu-Pt dealloyed nanoparticles [34] with high copper loading.…”

Section: Introductionmentioning

confidence: 93%

Theoretical investigation of the structures of unsupported 38-atom CuPt clusters

Guerrero-Jordan

Cabellos²,

Johnston

et al. 2018

Eur. Phys. J. B

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A genetic algorithm has been used to perform a global sampling of the potential energy surface in the search for the lowest-energy structures of unsupported 38-atom Cu-Pt clusters. Structural details of bimetallic Cu-Pt nanoparticles are analyzed as a function of their chemical composition and the parameters of the Gupta potential, which is used to mimic the interatomic interactions. The symmetrical weighting of all parameters used in this work strongly influences the chemical ordering patterns and, consequently, cluster morphologies. The most stable structures are those corresponding to potentials weighted toward Pt characteristics, leading to Cu-Pt mixing for a weighting factor of 0.7. This reproduces DFT results for Cu-Pt clusters of this size. For several weighting factor values, the Cu30Pt8 cluster exhibits slightly higher relative stability. The copper-rich Cu32Pt6 cluster was reoptimized at the DFT level to validate the reliability of the empirical approach, which predicts a Pt@Cu core-shell segregated cluster. A general increase of interatomic distances is observed in the DFT calculations, which is greater in the Pt core. After cluster relaxation, structural changes are identified through the pair distribution function. For the majority of weighting factors and compositions, the truncated octahedron geometry is energetically preferred at the Gupta potential level of theory.

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Empirical-potential global minima and DFT local minima of trimetallic Ag Au Pt (l+m+n= 13, 19, 33, 38) clusters

Cited by 33 publications

References 53 publications

A Global Optimizer for Nanoclusters

A Global Optimizer for Nanoclusters

First principles global optimization of metal clusters and nanoalloys

Theoretical investigation of the structures of unsupported 38-atom CuPt clusters

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