Search

In this study, we perform a systematic search to find the possible lowest energy structure of silicon nanoclusters Sin (n = 8-80) by means of an evolutionary algorithm. The fitness function for this search is the total energy of density functional tight binding (DFTB). To be on firm ground, we take several low energy structures of DFTB and perform further geometrical optimization by density functional theory (DFT). Then we choose structures with the lowest DFT total energy and compare them with the reported lowest energy structures in the literature. In our search, we found several lowest energy structures that were previously unreported. We further observe a geometrical transition at n = 27 from elongated to globular structures. In addition, the optical gap of the lowest energy structures is investigated by time-dependent DFTB (TD-DFTB) and time-dependent DFT (TD-DFT). The results show the same trend in TD-DFTB and TD-DFT for the optical gap. We also find a sudden drop in the optical gap at n = 27, precisely where the geometrical transition occurs. [ABSTRACT FROM AUTHOR]

In this paper, we employ an evolutionary algorithm along with the full-potential density functional theory (DFT) computations to perform a comprehensive search for the stable structures of stoichiometric (WS2)n nano-clusters (n = 1 - 9), within three different exchange-correlation functionals. Our results suggest that n = 5 and 8 are possible candidates for the low temperature magic sizes of WS2 nano-clusters while at temperatures above 500 Kelvin, n = 7 exhibits a comparable relative stability with n = 8. The electronic properties and energy gap of the lowest energy isomers were computed within several schemes, including semilocal Perdew-Burke-Ernzerhof and Becke- Lee-Yang-Parr functionals, hybrid B3LYP functional, many body based DFT+GW approach, ΔSCF method, and time dependent DFT calculations. Vibrational spectra of the lowest lying isomers, computed by the force constant method, are used to address IR spectra and thermal free energy of the clusters. Time dependent density functional calculation in a real time domain is applied to determine the full absorption spectra and optical gap of the lowest energy isomers of the WS2 nano-clusters. [ABSTRACT FROM AUTHOR]

Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating the energy of all structures using ordinary quantum ab initio methods can be significantly time-consuming. Machine learning can be a reliable alternative to ab initio methods. We calculate the energy of these compounds with an accuracy close to that of density functional theory calculations in a considerably shorter time using machine learning. In this study, we use kernel ridge regression and neural network to predict energy. In the KRR, we employ sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR. To implement the neural network, we use two important classes of application of the neural network in material science, including high-dimensional neural network and convolutional neural network based on crystal graph representation. We show that kernel ridge regression using MBTR and neural network using ACSF can provide better accuracy than other methods. [Display omitted] [ABSTRACT FROM AUTHOR]

The size-dependent properties of small pure copper and silver clusters and their alloys with one and two palladium atoms are studied by using full-potential density functional computations. The stable isomers of these clusters are identified and their magic numbers are determined via the analysis of the second difference of their minimized energy. We discuss that the doped Pd generally prefers the high coordination sites of the pure cluster. It is argued that Pd doping influences the structural cross over of these clusters. The GW correction is applied for more accurate determination of the electronic structure of the systems. [ABSTRACT FROM AUTHOR]