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Simulation of complex structuresusing massive parallel processing
Peter Ballo and Eva VitkovskaSlovak University of Technology
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Our aim: is to simulate and optimize complex structures
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Our experiences in the past:
Grain boundary simulation
BALLO, P., KIOUSSIS, N., LU, G.Materials Research Society Proceedings, Vol.634. : MRS, 2001, s. B3.14.1-7.Boston. USA, 27.11.-1.12.2000. BALLO, P., KIOUSSIS, M., LU, G. Phys. Rev. B, 64, 024104 (2001).
BALLO, P., SLUGEN, V. Phys. Rev. B, 65, 012107 (2002).
BALLO, P., SLUGEN, V. Computational Materials Science, 33, 491 (2005).
BALLO, P., DEGMOVÁ, J., SLUGEN, V.:Phys. Rev. B, 72, 064118 (2005).
BALLO, P., HARMATHA, L. Phys. Rev. B, 68,153201 (2003).
P.Ballo, D. Donoval, and L.Harmatha, IWCE 11, Vienna 2006
Electronic structure of defect in silicon
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What we need before we begin
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1. Well formulated problem -Size-Shape-Material-Surface/grain boundary-Temperature
2. Well chosen approximation -Structure-Interaction-Dynamics
3. Numerical technique -Molecular dynamics-Simulated annealing-Genetic algorithm-ab initio technique
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what we have improved
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GB
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More complex structures
From simple and ideal structures
To large and more realistic structures
Benefit: The possibility to describe more realistic structures
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New kind of interaction between atoms
From simple empirical interaction To more complex ab initio interaction
Benefit: ab initio interaction enables to describe new effect in the structure
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-15 -10 -5 0 5 10 15
1.0
1.5
2.0
2.5
5 Grain Boundary - BCC iron Magnetic moment
Ma
gn
etic
mo
me
nt (
Bo
hr
ma
gn
eto
n)
Distance from GB (A)
New effects on surfaces or grain boundaries
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From simulated annealing To genetic algorithm
Numerical methodology
Profit: genetic algorithm is more efficient for large systems and gives benefit as parallel
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New computational facilities
From small and inefficient systemGigabit internal network
To large and efficient systemInfinity internal network
Benefit: New kinds of parallel computationWe are going to increase the number of CPUs up to several hundreds
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Parallel Genetic Algorithm Structure Optimization Simulator
A new kind of massive parallel structure optimization simulator based on Genetic Algorithm
PAGASOSPAGASOS
We are working on
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Multi Scale Modeling on reactor steels
application for project ALEGRO (a Gas-Cooled Fast Reactor Demonstrator)
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MPS
Massive Parallel System
PAGASOS
PAGASOS
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LAMMPS
Magnetic properties – MFM (Magnetic Force Microscopy)
Magneto – structural properties – Barkhausen Noise
Structure Optimization – input for post computing
Simulation and Verification of PAS
An application ...
Output from post computing
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PAGASOSPAGASOS
LAMMPS
Barkhausen Noise noise in the magnetic output of a ferromagnetwhen the magnetizing force applied to it is change
Parallel Genetic Algorithm Structure Optimization Simulator
is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator.
a package whose based on Density Functional Theory (DFT), using pseudopotentials and a planewave or wavelet basis.
PAS The electron–positron annihilation process is the physical phenomenon relied on positron annihilation spectroscopy. It is also used as a method of measuring the Fermi surface, band structure and defects in metals.
