Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

Ignatov, Stanislav K.; Разуваев, А. Г.; Loginova, Anastasiia S.; Masunov, Artëm E.

doi:10.1021/acs.jpcc.9b08691

Cited by 22 publications

(9 citation statements)

References 71 publications

(115 reference statements)

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“…The SCG3 potential is based on the Sutton-Chen potential (SC), which is proposed as a less time-consuming alternative of GP. However, it was shown that both GP and SC potentials result in too simple and symmetric structures when applied to metallic (platinum) clusters. Therefore, it was proposed to augment this potential with the additional energy terms, which correct the interactions of metal atoms with the atoms of their second and third coordination spheres.…”

Section: Methodsmentioning

confidence: 99%

“…However, it was shown that both GP and SC potentials result in too simple and symmetric structures when applied to metallic (platinum) clusters. Therefore, it was proposed to augment this potential with the additional energy terms, which correct the interactions of metal atoms with the atoms of their second and third coordination spheres. It was demonstrated that the resulting potentials, especially SCG5 with five additional Gaussians, show significantly better agreement for the energy dependence on n compared to DFT results and more realistic cluster structures without excessive stability of icosahedral and other highly symmetric isomers, typical for the original GP and SC potentials .…”

Section: Methodsmentioning

confidence: 99%

“…The optimized parameter values are listed in Table S1 of the Supplementary Information. The parameter optimization was performed with the original program GLOBUS using the Monte-Carlo Simulated Annealing algorithm for the parameter optimization as described in refs , . The goal function for optimization included the atomization energies per atom for each cluster and the atomic coordinates.…”

Section: Methodsmentioning

confidence: 99%

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How Many Isomers Do Metallic Clusters Have? Case of Magnesium Clusters of up to 55 Atoms

et al. 2021

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About 9000 structures of magnesium clusters Mg n (n = 2–13) generated via different methods were optimized at the DFT levels in order to estimate the number of all possible stable structures that can exist for the given cluster size (∼820,000 PES points were explored in total). It was found that the number of possible cluster isomers N quickly grows with a number of atoms n; however, it is significantly lower than the number of possible nonisomorphic graph structures, which can be drawn for the given n. At the DFT potential energy surface, we found only 543 local minima corresponding to the isomers of Mg2–Mg13. The number of isomers obtained in the DFT optimizations grows with n approximately as n 4, whereas the N values extrapolated to the infinite generation process grow as n 8. The cluster geometries obtained from the global DFT optimization were then used to adjust two empirical potentials of Gupta type (GP) and modified Sutton-Chen type (SCG3) describing the interactions between the magnesium atoms. Using these potentials, the extensive sets of structures Mg2-Mg55 (up to 30,000 clusters for each n) were optimized to obtain the dependence of the cluster isomer count on n in the continuous range of n = 2–30 and for selected n up to n = 55. It was found that the SCG3 potential, which is closer to the DFT results, gives a number of possible isomers growing as approximately n 8.9, whereas GP potential results in the n 4.3 dependence.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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How Many Isomers Do Metallic Clusters Have? Case of Magnesium Clusters of up to 55 Atoms

et al. 2021

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“…Among metal clusters, platinum clusters are very important catalysts because of their excellent catalytic activity so that platinum clusters have attracted much interest for many chemical processes, , such as reduction of poisonous gases (CO, NO x , etc. ), , activation of alkanes, − and other chemical processes. , Therefore, it is significant to obtain the GM and low-lying energy structures of Pt clusters to better study the catalytic activity.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Global Optimization of Platinum Cluster Structures by Transfer Learning in a Deep Neural Network

Yang

Jiang

2023

J. Chem. Theory Comput.

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The global optimization of metal cluster structures is an important research field. The traditional deep neural network (T-DNN) global optimization method is a good way to find out the global minimum (GM) of metal cluster structures, but a large number of samples are required. We developed a new global optimization method which is the combination of the DNN and transfer learning (DNN-TL). The DNN-TL method transfers the DNN parameters of the small-sized cluster to the DNN of the large-sized cluster to greatly reduce the number of samples. For the global optimization of Pt9 and Pt13 clusters in this research, the T-DNN method requires about 3–10 times more samples than the DNN-TL method, and the DNN-TL method saves about 70–80% of time. We also found that the average amplitude of parameter changes in the T-DNN training is about 2 times larger than that in the DNN-TL training, which rationalizes the effectiveness of transfer learning. The average fitting errors of the DNN trained by the DNN-TL method can be even smaller than those by the T-DNN method because of the reliability of transfer learning. Finally, we successfully obtained the GM structures of Pt n (n = 8–14) clusters by the DNN-TL method.

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“…Application of computational approaches is also helpful for identification of activity trends which can minimize the frequency of trial and error experimental efforts. This has persuaded the adaption of various computational techniques including density functional theory (DFT) 15,16 empirical potential based methods 17,18 molecular dynamics (MD) 19,20 quantum mechanics/molecular mechanics (QM/MM) methods 21 and so on. These methods find their utilization in the nanocluster catalysis scenario for their applicability subjected to the size range, composition, dynamics and the approximations in reaction condition simulations.…”

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confidence: 99%

Computational strategies to address the catalytic activity of nanoclusters

Nair

Pathak

2020

WIREs Comput Mol Sci

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Metal nanoclusters have been emerged as imperative elements of heterogenous catalysis. Remarkably different properties from the bulk at the atomic level asserts construction of unique principles for understanding nanocluster catalysis. Computational studies abided by delicate theoretical framework are versatile tools for unveiling the structural, energetic, and mechanistic aspects of nanocluster‐based catalysis. Unlike other systems, nanoclusters feature properties derived from the unique surface structure, size dependence, and dynamics insisting fine‐tuning of computational approaches for accurate prediction of catalytic properties. Finding a balance between realistic simulation and computational affordability is of pressing priority. We highlight the urgency of computational models and practices to be updated by learning from the recent developments in this field. Focus is given to less explored factors governing the catalytic potential of nanoclusters. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Reaction Mechanisms and Catalysis

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Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

Cited by 22 publications

References 71 publications

How Many Isomers Do Metallic Clusters Have? Case of Magnesium Clusters of up to 55 Atoms

How Many Isomers Do Metallic Clusters Have? Case of Magnesium Clusters of up to 55 Atoms

Enhancing the Performance of Global Optimization of Platinum Cluster Structures by Transfer Learning in a Deep Neural Network

Computational strategies to address the catalytic activity of nanoclusters

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