Institute of Fundamental Technological Research

The variation of elastic constants stiffness coefficients with respect to different percentage ratios of defects in monolayer molybdenum disulfide (MLMoS2) is reported for a particular set of atomistic nanostructural characteristics. The common method suggested is to use conventional defects such as single vacancy or di vacancy, and the recent studies use stone-walled multiple defects for highlighting the differences in the mechanical and electronic properties of 2D materials. Modeling the size influence of monolayer MoS2 by generating defects which are randomly distributed for a different percentage from 0% to 25% is considered in the paper. In this work, the geometry of the monolayer MoS2 defects modeled as randomized over the domain are taken into account. For simulation, the molecular static method is adopted and study the effect of elastic stiffness parameters of the 2D MoS2 material. Our findings reveals that the expansion of defects concentration leads to a decrease in the elastic properties, the sheer decrease in the elastic properties is found at 25%. We also study the diffusion of Molybdenum (Mo) in Sulphur (S) layers of atoms within MoS2 with Mo antisite defects. The elastic constants dwindle in the case of antisite defects too, but when compared to pure defects, the reduction was to a smaller extent in monolayer MoS2. Nevertheless, the Mo diffusion in sulfur gets to be more and more isotropic with the increase in the defect concentrations and elastic stiffness decreases with antisite defects concentration up to 25%. The distribution of antisite defects plays a vital role in modulating Mo diffusion in sulfur. These results will be helpful and give insights in the design of 2D materials.

mono-layer MoS2, mechanical properties, molecular statics/dynamics, defects, random distributed defects

A potentially new, single-atom thick semiconducting 2D-graphene-like material, called Anisotropic-cyclicgraphene, has been generated by the two stage searching strategy linking molecular and ab initio approach. The candidate was derived from the evolutionary-based algorithm and molecular simulations was then profoundly analysed using first-principles density functional theory from the structural, mechanical, phonon, and electronic properties point of view. The proposed polymorph of graphene (rP16-P1m1) is mechanically, dynamically, and thermally stable and can achieve semiconducting with a direct band gap of 0.829 eV.

carbon, graphene, graphyne, ab initio calculations, Semiconductors

Two potentially new, 2D-graphene-like materials have been generated by the two stage searching strategy combining molecular and ab initio approach. The two candidates obtained from the evolutionary based algorithm and molecular calculations were then in depth analysed using first-principles Density Functional Theory from the mechanical, structural, phonon and electronic properties point of view. Both proposed polymorphs of graphene (oP8-P2mm) are mechanically and dynamically stable and can be metallic-like.

Carbon, Graphene, Ab initio calculations, Mechanical properties, Elastic properties

The following article presents the description and application of an algorithm for optimal searching for the new stable atomic arrangements of two-dimensional graphenelike carbon lattices with predefined mechanical properties. The proposed method combines the evolutionary algorithm and the conjugate-gradient optimization. The main goal of the optimization is to find stable arrangements of carbon atoms placed in the unit cell with imposed periodic boundary conditions, which reveal desired mechanical properties. Examples of the newly obtained models of the flat, carbon materials are presented. Their mechanical properties are additionally validated during the simulation of the tensile tests using molecular dynamics.

2D atomic structures, graphenelike materials, hybrid algorithm, evolutionary algorithm, mechanical properties

The pap er describes an application of the hybrid intelligent algorithm to optimal searching for new, stable atomic arrangements of 2D graphene -like carbon lattices. The proposed approach combines the parallel evolutionary algorithm and the conjugated -gradient optimization technique. The main goal is to find stable arrangements of carbon atoms under certain imposed condi tions such as density, shape and size of the unit cell and also predefined mechanical properties. The nanostructure is considered a discrete atomic model and interactions between atoms are modeled using the AIREBO potential, especially developed for carbon. The parallel approach is used in computations. Validation of the obtaine d results and examples of new models of the new grapheme -like materials are presented

Hybrid intelligent algorithm, 2D nanostructures, new grapheme-like materials

The paper is devoted to the optimization of energy of carbon based atomic structure with use of the memetic algorithm. The graphene like atoms structure is coded into floating point genes and underwent evolutionary changes. The global optimization algorithm is supported by local gradient based improvement of chromosomes. The optimization problem is solved with the use of Intel PHI (Intel Many Integrated Core Architecture – Intel MIC). The example of optimization and speedup measurement for parallel optimization are given in the paper.

Parallel computing, Intel PHI, Optimization, Graphene-like materials

The article describes the application of a Hybrid Parallel Evolutionary Algorithm (HPEA) to optimal searching for new, stable atomic arrangements of two-dimensional graphene-like carbon lattices. The proposed approach combines the parallel evolutionary algorithm and the conjugated-gradient optimization technique. The main goal of the optimization is to find stable arrangements of carbon atoms under certain imposed conditions (e.g. density, shape and size of the unit cell). The fitness function is formulated as the total potential energy of an atomic system. The optimized structure is considered as a discrete atomic model and interactions between atoms are modeled using the AIREBO potential, especially developed for carbon and hydrocarbon materials. The parallel approach used in computations allows significant reduction of computation time. Validation of the obtained results and examples of the models of the new 2D materials obtained using the described algorithm are presented, along with their mechanical properties.

Graphene-like materials, Optimization at the nano level, AIREBO potential, Evolutionary algorithm, Parallel computing

Methods of creating digital material representations of polycrystalline structures based on molecular dynamics (MD) simulations are presented in this paper. All simulations are performed using the massively parallel MD solver and the canonical ensemble. The simple pair-wise model and the more sophisticated many-body atomic potential model are utilized. All of the unique features and parameters (e.g., size and crystallographic orientation grain) of each approach, along with the results of the simulations, are discussed in detail and illustrated with proper numerical examples. Additionally, a comparison of the mechanical properties between the ideal monocrystal structure and a series of obtained polycrystalline structures is included, along with a description of the algorithm used in the computation of the mechanical properties and the stress-strain relationships.

nanomechanics, mono- and polycrystals, molecular dynamics, digital material representation, mechanical properties

Development and application of the hybrid parallel evolutionary-conjugated gradient algorithm for searching for new, stable atomic arrangements of the two-dimensional graphene-like carbon lattices was described in this paper. The main goal of the optimization is to find stable arrangements of carbon atoms under imposed conditions (e.g. density, shape and size of the unit cell). Such configurations correspond to the minimal values of the total potential energy of the atomic system. Thus, the fitness function is formulated as the total potential energy of the atoms. Interactions between carbon atoms are modeled using Adaptive Intermolecular Reactive Bond Order potential. The parallel approach used in computations allows significant reduction of computation time. Validation of the achieved results and example of the model of new 2D material obtained using presented method were presented in this paper. The numerical scalability tests of the algorithm were performed on the IBM BlueGene/Q supercomputer.

nano-scale modeling, carbon materials, AIREBO potential, evolutionary algorithm, conjugate gradient minimization, parallel computing

The flat, two dimensional materials play important role in the research and industrial applications in the last 15 years. The new materials with flat atomic structures are discovered almost every month. The focus of the paper is on the discrete modellingof the single layer molybdenum disulphide based material (SLMoS2). Two methods, based on the molecular statics and molecular dynamics of estimation of materials properties and numerical simulations at the nanolevel are described and discussed.

optimization, material properties, MoS2, molecular statics

The flat, two dimensional materials play important role in the research and industrial applications in the last 15 years. The new materials with flat atomic structures are discovered every month. The focus of the paper is on the modelling of the single layer molybdenum disulphide based material. The numerical simulations and mechanical material properties are described and discussed.

molybdenum, two dimensional materials, molecular modelling, MoS2

carbon nano-structures, atom structures, memetic algorithms

carbon nano-structures, conjugated gradient method, evolutionary algorithm