Institute of Fundamental Technological Research

Various technological challenges are essentially material problems in our times. New functional and functional graded nanomaterials are constructed of components with predefined properties. The design of nanostructures with predefined mechanical properties was considered in this paper. This study applies the evolutionary algorithm (EA) to the optimization problem in the design of nanomaterials. The optimal design combined EA with molecular dynamics to identify the size of the void for the prescribed elastic properties in monolayer 2D MoS2 nanostructures. The numerical results show that the proposed EA and the use of optimization method allowed accurately obtaining nanostructures with predefined mechanical material properties by introducing elliptical voids in the 2D MoS2 nanosheet

optimization, MoS2 , nanostructure, mechanical properties

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

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 paper is devoted to the identification of microscale heat-transfer parameters. The numerical modeling of short-pulse laser interaction with thin metal films is considered. The hyperbolic two-temperature model describing the temporal and spatial evolution of the lattice and electrons temperatures in the irradiated metal is applied. This model consists of four equations: two equations concern the electron and lattice temperatures; the later ones determine the dependencies between heat fluxes and temperatures. The short-pulse laser interaction with the film is taken into account by introducing an internal volumetric heat source to the equation describing the electron temperature. The equations concerning the electrons and lattice temperatures are joined by coupling factor G, which characterizes the energy exchange between phonons and electrons. The relations between electron heat flux and electron temperature and between the lattice heat flux and lattice temperature contain the parameters ?e and ?l, respectively. The parameter ?e is the relaxation time of free electrons in metals; the parameter ?l is the relaxation time in phonon collisions. The one-dimensional problem is analyzed. (Heat transfer in the direction perpendicular to the thin film is taken into account.) The nonflux conditions can be accepted at the front surface irradiated by a laser pulse and the back surface. The initial conditions are also assumed. The direct problem is solved by the explicit scheme of the finite difference method. The results of the computations are partially compared with the experimental data available in literature. The inverse problem discussed here consists in the simultaneous identification of three parameters, namely, the coupling factor G and relaxation times ?e and ?l. To solve such a problem, the electron temperature history at the irradiated surface of the thin film is taken into account. The inverse problems can be formulated as optimization problems and solved by means of bioinspired algorithms. The objective function is formulated on the basis of the known measured and numerical simulated values of temperature. The minimization of the objective function allows one to find the design variables vector, which may contain the parameters of the coupling factor and time coefficients in the presented case. The inverse problems are ill-defined problems, and the identification may lead to different results with the same objective function value. The objective function can have many local minima, and therefore the bioinspired algorithm is used in the paper.

multiscale modeling, two-temperature model, identification, bioinspired algorithms

The paper is devoted to multiscale identification of material properties in microscale. The identification process allows one to identify properties (like material constants, geometry) in microscale on the basis of measurements performed for macroscale. The presented approach assumes stochastic material properties in microscale. The identification problem is formulated as minimization of a functional which represents a distance between measured and theoretical values of displacements and strains. The Monte Carlo method combined with the finite element method is used to obtain theoretical displacements and strains values. The identification problem is solved with use of an evolutionary algorithm.

identification, stochastic, multiscale, FEM

The paper presents a methodology of the multiscale bone mode ling in which the task of identification of material parameters plays the crucial role. A two-scale analysis of the bone is considered and the problem of identification, formulated as an inverse problem, is examined as an important stage of the modelling process. The human femur bone, built form cancellous and cortical bone, is taken as an example of an n osseous tissue, and the computational multiscale approach is considered. The methodology presented in the paper allows one to analyze the two-scale model with the use of computational homogenization. The representative volume element (RVE) is created for the microstructure of the basis of micro-CT scans. The macro and micro model analyses are performed by using the finite element method. The identification of trabeculae material parameters on the micro-level is considered as the minimization problem which is solved using evolutionary computing.

multiscale modeling of bone, computational homogenization, identification of material parameters

Shape optimization of heat conducting, elastic bodies subjected to thermal and standard loads is considered. Interaction of stress and temperature fields is modelled using the formulation of steady state thermoelasticity. The presence of heat radiation with mutual irradiation of the boundaries and the presence of shadow zones is taken into account. Evolutionary algorithm is used to evaluate the optimal shape. The boundary element method is applied to discretize the thermoelasticity, conduction and radiation problems.

Evolutionary algorithm, Shape optimization, Thermoelasticity, Radiation, Stress, BEM, Parallel computing

The aim of this work is to present a method for optimizing the mechanical properties of a nanostructure based on Silicene. Optimization is carried out using a parallel evolutionary algorithm and parallel Molecular Dynamics analyses used to determine material properties. Due to high computational costs, optimization was carried out using one of the fastest supercomputers in the world – LUMI installed in CSC in Kajaani, Finland.

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.

The paper is devoted to an application of the swarm methods and the finite element method to optimization of the stiffeners location in the 2-D structures (plane stress, bending plates and shells). The structures are optimized for the stress and displacement criteria. The numerical examples demonstrate that the method based on swarm computation is an effective technique for solving computer aided optimal design.

swarm algorithms, optimization, finite element method (FEM), bars, plane stress, bending plates, shells

Methods of computational intelligence, 2D nanostructures, Molecular dynamics

The analysis of nanostructures at the atomic level requires knowledge of interatomic interactions. There is a lot of data available in the literature for potentials, but it is often necessary to adjust the parameters of interatomic interactions for unusual structures, e.g. two dimensional materials. The aim of this work is to present a method for the optimal selection of interatomic parameters using machine learning techniques. Reinforced learning is one of the groups of methods that allow the use of artificial intelligence in the selection of parameters or models of real systems. The article uses the Monte Carlo Tree Search method [1,2] combined with local and global optimization algorithms for the optimal selection of silicon potential parameters for two dimensional structures.
The research used Molecular Dynamics (MD) to simulate nanostructures, and based on the results, nanostructure parameters were determined and compared with standard data from the literature. The aim of the optimization was to ensure the greatest possible compliance of the nanostructure parameters with the reference data. MD analyses were performed using the LAMMPS package [3].
The full article presents a description of the method as well as numerical examples for the two dimensional structure of silicon – silicene.

Reinforced Learning, Optimization, Interatomic potential, Nanostructure

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