Todays Talk What is atomic scale simulation? Role of atomic
simulation in nano-materials research Brief survey of some cases
Where should we go?
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Computer Simulation ( ) 16KeV Au 4 Cluster on Au (111)
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Time Evolution of R i and v i Molecular Dynamic Simulation i
Empirical Approach First Principle Approach Interatomic
Potentials
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R. Feynman, Lectures on Physics, Ch. 7 & 9 (1963) Theory
and Observations (Newtonian Mechanics) Motion of a Mass on a Spring
Orbit of Sirius Double Star
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Laplaces Dream (1814) Pierre-Simon Laplace (1749-1827) Given
for one instant, an intelligence which could comprehend all the
forces by which nature is animated and the respective situation of
the beings , nothing would be uncertain and the future, as the
past, would be present to its eyes.
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The intelligence in 21 st Century High computing power at low
cost High performance visualization tools
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New Era of Computer Simulation C-plant @ Sandia National Lab.
Beowulf Cluster @ CALTECH Alpha Cluster @ SAITAvalon @ Los Alamos
National Lab.
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80 Execution Nodes X2 Pentium III (850~2050MHz) connected by
100Mbps Ethernet and Myrinet 66 Gbyte RAM 4.9 Terabyte HDD 2 Head
Execution Nodes X4 Pentium III Xeon (700,2000MHz) for Head
Execution 4Gbyte RAM 3,280Gbyte HDD 100Gflops KIST Beowulf
System
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KIST 1024 CPU Cluster System
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GRID Environment
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Moors Law in Atomic Simulation Empirical MD Number of atoms has
doubled every 19 months. 864 atoms in 1964 (A. Rahman) 6.44 billion
atoms in 2000 First Principle MD Number of atoms has doubled every
12 months. 8 atoms in 1985 (R. Car & M. Parrinello) 111,000
atoms in 2000
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The intelligence in 21 st Century High computing power at low
cost High performance visualization tools
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Telescope : Galilei (1610) Microscope : Leeuwenhoek (1674) :
Golgi & Cajal (1906 Nobel Prize) Neuroscience : Millikan (1923
Nobel Prize) STM / AFM : Binnig & Rohrer (1986 Nobel Prize)
Nano-Technology
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Min Max 4 3 2 1 0 5 In case of 75 eV
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Virtual Reality & Visualization
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Nanomaterials
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~ nm Characteristics of Nanotechnology Continuum media
hypothesis is not allowed. Diffusion & Mechanics Band
Theory
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Case I : Size Dependent Properties Atomic Orbitals N=1
Molecules N=2 Clusters N=10 Q-Size Particles N=2,000 Semiconductor
N>>2,000 h Energy h Conduction Band Valence Band Vacuum CdSe
Nanoparticles Smaller Size
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Case II : Scale Down Issues 2~4nm 0.13 m 10 nm Kinetics based
on continuum media hypothesis is not sufficient.
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Chracteristics of Nanotechnology Continuum media hypothesis is
not allowed. Large fraction of the atom lies at the surface or
interface. Abnormal Wetting Abnormal Melting of Nano Particles
Chemical Instabilities
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Case IV : GMR Spin Valve Major Materials Issue is the
interfacial structure and chemical diffusion in atomic scale Major
Materials Issue is the interfacial structure and chemical diffusion
in atomic scale
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Nanoscience or Nanotechnology , Needs Atomic Scale
Understandings on the Structure, the Kinetics and the Properties
Needs Atomic Scale Understandings on the Structure, the Kinetics
and the Properties
Atomic Scale Simulation of Interfacial Intermixing during Low
Temperature Deposition in Co-Al System
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Magnetic RAM (MRAM) 1 nm Properties of MRAM are largely depends
on the Interface Structures of Metal/Metal or Metal/Insulator
Controlling & Understanding The atomic behavior at the
interface are fundamental to improve the performance of the
nano-devices!
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Conventional Thin Film Growth Model Conventional thin film
growth model simply assumes that intermixing between the adatom and
the substrate is negligible. Conventional thin film growth model
simply assumes that intermixing between the adatom and the
substrate is negligible.
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Adatom (0.1eV, normal incident) Substrate Program : XMD 2.5.30
x,y-axis : Periodic Boundary Condition z-axis : Open Surface dt :
0.5fs, calculation time : 5ps/atom [100] [001] [010] z y x 300K
Initial Temperature 300K Constant Temperature Fix Position
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Depostion Behavior on (001) Reaction Coordinate Co on Al
(001)
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Deposition Behavior on (001) Al on Co (001)
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Deposition Behavior on (001) Al on Al (100) Al on Al (001)
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Thin Film Growth Conventional thin film growth model assumes
negligible intermixing between the adatom and the substrate atom.
In nano-scale processes, the model need to be extended to consider
the atomic intermixing at the interface. Conventional Thin Film
Growth Model Calculations of the acceleration of adatom and the
activation barrier for the intermixing can provide a criteria for
the atomic intermixing.
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ABDC {111} plane Tensile Test of Cu Nanowires Computational
Semiconductor Technology Lab.
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Electron Emission from CNT ,
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Array of sub-nano Ag Wire Self Assembling of CHQ Nanotube
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Search for New DMS Materials SiC:TM or AlN:TM DOS of AlN
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Search for New DMS Materials SiC:TM or AlN:TM DOS of AlN Half
Metal!!
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Spin as new degree of freedom in quantum device structures
Combine nonvolatile character with band gap engineering New
Functionality Motivation spin-LED FM p+ ~ ~ ~ ~ ~ ~ circularly
polarized output 2DEG transport 2DEG V g spin-FET source gate drain
single transistor nonvolatile memory Spintronics
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Role of Computational Modeling Provide physical intuition and
insight where the continuum world is replaced by the granularity of
the atomic world. Bridge the Gap between Fundamental Materials
Science and Materials Engineering Provide virtual experimental
tools where the physical experiment or analysis fails. Allow
fundamental theory (i.e.quantum mechanics) to be applied to a
complex problem.
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Importance of Modeling & Simulation The emergence of new
behaviors and processes in nanostructures, nanodevices and
nanosystems creates an urgent need for theory, modeling,
large-scale computer simulation and design tools and infrastructure
in order to understand, control and accelerate the development in
new nano scale regimes and systems. NSF announcement for
multi-scale, multi-phenomena theory, modeling and simulation at
nanoscle activity (2000)
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Materials Science in 21 st Century Computational simulation was
frequently emphasized in many articles. H. Gleiter : Nanostructured
Materials W.J. Boettinger et al : Solidification Microstructures J.
Hafner : Atomic-scale Computational Materials Science A. Needleman
: Computational Mechanics in mesoscale
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Hierarchy of Computer Simulation Fundamental Models - Ab initio
MD - First Principle Calculation Atomic Level Simulation - Monte
Carlo Approach - Classical MD Engineering Design ns fs ss ms ps min
TIME DISTANCE 1A10A100A 1m1m 1mm Continuum Models - FEM/FDM - Monte
Carlo Approach
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First Principle Calculation Classical MD Continuum Simulation
Multiscale Simulation
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Multi-scale Approaches In Case of Fracture
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Technologies Products 200020102020 National TRM for Modeling
& Simulation Scale Molecular Manipulation Smart Nanosystem
& Process Designer Multiscale Materials Simulation Empirical MD
Quantum MD Mesoscale Simul. Virtual Reality & Smart MMII High
Performance Computing & Algorithm Cluster Computing Smart
Parallel Algorithm Quantum Computing Integrated Simulation
Technology Multiscale Simulator Nano Materials & System DB
Source : ( , 2002)
Experimental Research Groups Multiscale Interfacing Algorithm
Application I/F Cluster Supercomputer & Computing Scale
Inter-Scale Interfacing First Principle Simulation Classical MD and
MC Simulation Force Field DB Mesoscale and Continuum Simulation
Device Simulation Multiscale Simulation Model
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Within five to ten years, there must be robust tools for
quantitative understanding of structure and dynamics at the
nanoscale, without which the scientific community will have missed
many scientific opportunities as well as a broad range of
nanotechnology applications.