STUDY OF NANO-SYSTEMS FOR COMPUTER SIMULATIONS

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 5 (September-October 2015), PP. 63-68
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STUDY OF NANO-SYSTEMS FOR COMPUTER
SIMULATIONS
Asst.lec. Abdullah Hasan Jabbar
Working Asst. Lecturer in the Islamic College University,
Department of Computer Engineering Techniques . Iraq
physics1984@yahoo.com
Abstract— in the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result.
Index Terms Nano-Systems, Computer-Simulations, global
optimization method, Molecular Dynamics, hardware/software
design Space.
I. INTRODUCTION
As we enter in to the new century it is
probably as good a time as any to look ahead and
try to glimpse future trends in our society. With
the abundance of powerful personal computer as
well as plentiful supercomputer, time, available
to researchers. Before that we should know about
nanotechnology. In the Nanotechnology the
manipulation of matter on an atomic and
molecular scale. Generally, nanotechnology
works with materials, devices, and other
structures with at least one dimension sized from
1 to 100 nanometers. The design,
characterization, production and application of
materials, devices and systems by controlling
shape and size of the nanoscale [1]. The
computer simulation techniques are widely used
for computational nano-technology[2]. The
frequently used simulation approaches are Monte
Carl, and Molecular Dynamics methodsis the
manipulation of matter on a atomic and
molecular scale. Generally, nanotechnology
works with materials, devices, and other
structures with at least one dimension sized from
1 to 100 nanometers. Nanotechnology is very
diverse, ranging from extensions of
conventional device physics to completely new
approaches based upon molecular self assembly
from developing new materials with dimensions
on the Nanoscale to direct control of matter on
the atomic scale. The computer- based simulation
methods, developed for Nano-systems, generally
consist of a computational procedure performed
of few atoms or molecules confined in a small
geometrical space. This geometrical space in
which the simulation is performed is termed as
cell. In the subsequent section, a brief
classification of simulation methods based on
Accuracy, Computational Time. This site
features information about discrete event system
modeling and simulation. It includes discussions
on descriptive simulation modeling,
programming commands, techniques for
sensitivity estimation, optimization and goal-
seeking by simulation ,and what-if analysis.
Advancements in computing power, availability
of PC-based modeling and simulation, and
efficient computational methodology are
allowing leading-edge of prescriptive simulation
modeling such as optimization to pursue
investigations in systems analysis, design, and
control processes that were previously beyond
reach of the modelers and decision makers.
engineering mechanics provides excellent

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theoretical descriptions for the rational design of
materials and accurate lifetime prediction of
mechanical structures. This approach deals with
continuous quantities such as strain field that are
functions of both space and time[7]. Constitutive
relations such as Hooke’s law for deformation
and Coulomb’s law for friction describe the
relationships between these macroscopic fields.
These constitutive equations contain material-
specific parameters such as elastic module and
friction coefficients, which are often size
dependent. For example, the mechanical strength
of materials is inversely proportional to the
square root of the grain size, according to the
Hall-Petch relationship. Such scaling laws are
usually validated experimentally at length scales
above a micron, but interest is growing in
extending constitutive relations and scaling laws
down to a few nanometers. This is because many
experts believe that by reducing the structural
scale (such as grain sizes) to the nanometer
range, we can extend material properties such as
strength and toughness beyond the current
engineering-materials limit. In addition,
widespread use of Nano electromechanical
systems.
A. Classification of simulation methods based on accuracy
and computational time
The computer based simulation
method, being developed for nano-system,
generally consist for computational procedure
perform on a limited number of atoms,
molecules, molecular building blocks or
macromolecules confined to a limited, but small,
geometrical space[12]. Generally the cell in
which the simulation is performed could be
replicated in all spatial dimensions, generating its
own periodic images. computer based methods
used for simulation of various properties of nano
scale systems differ in their level of accuracy and
time-complexity to perform such calculations.
Based on it, the required time scale for these
methods can be from tens of picoseconds to few
microseconds or more (classical molecular
dynamics simulation). There are also methods
which require very long computational time such
as cluster growth and may require super
computers to achieve fast results. Based on these
facts we may classify the methods into following
groups (i) Methods with highest degree of
accuracy (ii) Methods with second highest degree
of accuracy (iii) Semi-empirical method and (iv)
Stochastic method
The most important input to such
computation is the antiparticle energy/force
function for interaction between the entities
composing the nano-system. Accuracy
administered computer simulations can help in
three different ways:
1-they can be used to compare and
evaluate various molecular-based theoretical
models.
2-they can help the evaluate and direct an
experimental procedure for nano-system.
3- An ultimate use of computer
simulations is its possible replacement of an
experiment which otherwise may not be possible
with the present state of the technology or may
be too costly, but provided accurate
intermolecular potentials are available to be use
in their development.
B. Method with highest degree of accuracy
- Input: Atomic species, coordinate,
system’s symmetry, interaction
parameter.
- Output: Total energy, excitation energy
and spin densities, force on atoms
- Purpose: Investigation of both electronic
and atomic ground state, optical and
magnetic properties of weakly interacting
and also strongly interacting correlated
systems
C. Method with second highest degree of accuracy
- Input: atomic species and their coordinate
and symmetry of the structure; eventually
for the species considered.
- Output: Total energy, charge and spin
densities, forces on atoms, electron
energy eigen values, capability of doing
Molecular Dynamics, vibration modes

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and phonon spectrum.
- Purpose: Accurate calculations of ground
state structure by local optimization;
Calculation of mechanical, magnetic and
optical properties of small clusters and
perfect crystals of weakly interacting
electron systems, estimation of reaction
barrier and paths.
D. Semi-empirical methods
- Input: Atomic species, their coordinates;
parameters of the inter-particle potential,
temperature and parameters of the
thermostat or other thermo-dynamic
variables.
- Output: Output of Tight- Binding(TB):
Total energy, charge and spin densities,
force on atoms, particle trajectories,
phonon calculation; mechanical magnetic
and optical properties of clusters and
crystals.
- Purpose: Search for ground state structure
by GA, Simulated Annealing (SA) or
local optimization if a good guess for the
structure is known; simulation of growth
or some reaction mechanisms; calculation
of response functions.
Stochastic method: There are several
methods that use stochastic representations of
some or all of the physical processes responsible
for ground shaking. In this paper I review the
particular stochastic method that I and a number
of others developed in the last several decades.
The paper includes a few new figures and an
improvement in the calculation of random
vibration results that previously appeared only in
an USGS open-file report, Other authors have
published papers applying the stochastic method
and extending the method in various ways.
Purpose: Investigation of long timescale
non-equilibrium phenomena such as transport,
growth, diffusion, annealing, reaction
mechanisms and also calculation of equilibrium
quantities and thermodynamic properties.
E. Molecular dynamics simulation methods
The two basic used simulation
approaches are Monte Carlo (MC) and Molecular
Dynamics (MD) methods. All the other various
simulation methods come from these two basic
methods. A brief over-view with areas of
application of the both are discussed below.
These concepts are essentially required to
understand the methodology of classification of
Computer-Based Simulation methods based on
accuracy and time-complexity. MC method uses
random numbers to perform calculations. There
are many areas of application of MC Methods
including Nano-material. Some important areas
where we apply MC method are:- (i) Estimation
of large-dimensional integrals (ii) Generating
thermodynamic ensembles in order to compute
thermal averages of physical equilibrium
quantities of interest and simulation of non-
equilibrium phenomena such as growth and (ii)
Computation of distribution functions out of
equilibrium known as Kinetic Monte Carlo [4].
MD deals with predicting the trajectories of
atoms subject to their mutual interactions and
eventually an external potential. Some important
areas of application of MD are: - (i) Computation
of transport properties such as response
functions, viscosity, elastic module and thermal
conductivity (ii) Thermodynamic properties such
as total energy and heat capacity and (iii)
Dynamical properties such as phonon spectra.
F. Global Optimization Methods
A much more challenging task than local
optimization methods is to find the global
minimum of a multi-valley energy landscape as
shown in fig.1. global optimization problems
involving a given cost function (minimization of
energy or maximization of entropy) arise in
many simulation problems dealing with Nano-
systems. This subject has received a great deal of
attention in recent year, mostly due to the rapid
increase in computer power. The symbolic
picture shown in fig.1. provides a rather simple
tow- dimensional example of the global
optimization paradigm.

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A number of computational algorithms
have been develop recently for global
optimization of molecular simulations. Among
those algorithms the simulated annealing and the
genetic algorithms have found more applications
in structural and dynamic simulation
optimization of molecular clusters.
Fig.1. A two-dimensional example of
global optimization of molecules energy E(x,y).
the objective here is to locate the coordinates
(x,y) for which the molecules energy has its
absolute minimum.
This algorithm is basically search
algorithm based on the machines of natural
selection and nature genetics. It is the balance
between efficiency and efficacy necessary for
survival in many different environments. In order
for GA to surpass their more traditional in the
quest for robustness, GA must differ in some
fundamental ways. GA are different from more
normal optimization and search procedures in
four ways:
 GA work with a coding of the parameter
set, not the parameters themselves.
 GA search from a population of point not
a single point.
 GA use pay off (objective function) not
derivatives or other auxiliary knowledge.
 GA use probabilistic transition rules, not
deterministic rules.
G. Optimization for Codesign to Molecular Dynamics
There are two applications address
issues of the response of materials in extreme
conditions and enabling the design of more
effective and safe fission power plants,
respectively. Hardware-Software codesign,
perceived as a prerequisite exascale computing,
needs to be put on a sound scientific basis such
that design decisions for both hardware and
software do not need to be made based on
colloquial heuristic insights, but rather follow an
established scientific procedure by sufficiently
thorough search of realizable hardware and
software options. Fig.1 illustrates our approach,
where the left hardware design and the right
software design boxes define a vast space of
hardware and software solutions[5], whose
combinations lead to performance prediction, the
results of which in turn guide an optimization
method towards new hw/sw solution to be tested.
This resulting iterative process can be analyzed
in formal and informal settings, thus opening
doors to established optimization and analysis
techniques, while at the same time incorporation
sometimes superior but informal human
ingenuity. The key research efforts in this
approach are:
 Efficient enumeration method for the both
hardware and software.
 Performance predication methods.
 Optimization methods to search the
design spaces.
H. Simulation-based Optimization Techniques
Discrete event simulation is the
primary analysis tool for designing complex
Nano- systems. Simulation, however, must be
linked with a optimization techniques to be
effectively used for Nano-systems design.
Simulations used as experiments:
1-Possible in case of coincidence between
purposes of simulations and experiments
2-Discovering new explanatory
hypotheses, confirming or refusing theories,
choosing among competing hypotheses
3-Simulation with no experimental
purposes in mind (simulation of a protein folding
process for didactical Purposes)

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I. Computer- Simulations and Results Obtained
In this paper work declares that a soft
computing tool, GA is used to get the optimized
system parameters of Ga As QW for a desired
high frequency response characterized by a
cutoff frequency (f3dB). QW is obtained for a
high frequency under hot electron condition. In
GA, a fitness function is the main criteria for
reproduction. The fitness values are used to favor
high fitness individuals over low fitness
individuals to take part in the process of
reproduction. in this work the application of GA,
we get the f3dB for a semiconductor quantum
structure for its different system parameters.
Then we get cut off frequency it was low
frequency value and one particular parameter of
the system where the other parameters are
optimized by the GA. By taking the other
parameters in one form, we can be able to find
the fitness values. These fitness values are
converted to binary form and then proceed for
further GA operation. After reproduction, simple
crossover may proceed in the following steps.
We note that the cut of frequency of mutation to
get good results in GA studies.
II. CONCLUSIONS
The classification of simulation method
presented in this paper important of the role of
molecular dynamics and computer simulations
in nanotechnology as well recognized for several
reason:
1- Such simulation techniques will allow us
to develop some fundamental
understanding behavior of nano-systems
for which there.
2- Since in a nano-system the number of
particles involved is rather small and
direct measurement of their collective
behavior has not been well developed yet,
computer simulations could help
appreciable.
3- Computer simulation can produce data for
testing and development of analytic
predictive models on nano-systems.
4- demonstrates the various processes
through which computer based
simulations and optimizations are used in
various field starting from classical to
nano-levels.
5- The same will also help the reader to
choose the best method suitable for a
particular application of their interest. To
give a better view we have given one
example of computational technique that
we have used for nano device simulation
from one of our experimental work.
REFERENCES
[1] B.D. Cullity, Elements of X-ray Diffraction (Addison-
Wesley Publishing Company, Inc., 1956).
[2]. Narsingh Deo, System Simulation with Digital
Computer, PHI Pvt. Ltd. (2001)
[3] Neel L 1961 C. R. Acad. Sci. Paris 252 4075, 253 9,
203, 1286
[4]. Lakhtakia A., A Handbook on Nanotechnology
Nanometer Structures Theory, Modeling and Simulation,
PHI Pvt. Ltd (2007).
[5]. Fox Keller, E. (2003) “Models, Simulation, and
‘computer experiments’, in Radder H. (ed.) The Philosophy
of Scientific Experimentation, Pittsburgh University
Press,198-21
[6]. Chandra S., Computer Application in Physics, Narosa
Publishing House (2003).
[7]. Jorge J. More' et al, Optimization Software Guide,
SIAM Publications (1993).
[8]. Das S. C., Neuro-Genetic Approach for Optimizing
Parameters for the Polar Semiconductor, Proceedings of
NATCOMNNAMTECH 2007.
[9] M. Sherif El-Eskandarany, Satoru Ishihara, Wei Zhang
and A. Inoue, Met. Trans. 36 A (2005) pp. 141-147.
[10] S. Ogata et al., “Hybrid Finite-Element/Molecular-
Dynamics/Electronic- Density-Functional Approach to
Materials Simulations on Parallel Computers,” to be
published in Computer Physics Comm.
[11]R. Car and M. Parrinello, “Unified Approach for
Molecular Dynamics and Density Functional Theory,”
Physical Rev. Letters, vol. 55, 1985, pp. 2471–2474.
[12]. Lakhtakia A., A Handbook on Nanotechnology
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PHI Pvt. Ltd (2007).
[13] Sheng-Hui Lin(2008)"Electrochromic properties of
nano-structured nickel oxide thin film prepared by spray
pyrolysis method". Volume 254, Issue 7, 30 January 2008,
Pages 2017–2022

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Asst. Lecture, Abdullah Hasan Jabbar Received his M.Sc in
Physics Dpartment,
College of basic science In SHIATS University,
Allahabad Uttar Pradesh, India- 211007
Contact:+9647803520582
Email- Physics1984@yahoo.com

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