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Simulation of complex structuresusing massive parallel processing

Peter Ballo and Eva VitkovskaSlovak University of Technology

Bratislavapeter.ballo@stuba.sk

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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.