Molecular Dynamics of Organic Materials

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2nd Workshop Minneapolis, August 5-10, 2007
A Visualization Framework For Earth Materials
Studies Bijaya Bahadur Karki Graduate
Students Dipesh Bhattarai and Gaurav
Khanduja Department of Computer
Science Department of Geology and Geophysics
Louisiana State University, Baton Rouge, LA
70803
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Studying Materials Problems
Simulation algorithms
PWscf, VASP PCMD Parallel and distributed
computing
Tezpur (15.3 TFlops, 360 nodes 2 dual-core
processor) Queen Bee (50.7 TFlops, 680 nodes 2
quad-core processors)
Compute- and data-intensive applications
Mantle materials Silicates and oxides Rheology
Liquids
Visualization algorithms
Massive multivariate data MDV STMR ReVis
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Visualization Definition

• Process of making a computer image for gaining
  insight onto data/information
• Transform abstract, physical data/information to
  a form that can be seen (i.e., visual
  representation)
• Enhance cognitive process

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Visualizing Materials Data

• Properties/processes of interest
• Microscopic
• Atomic structure, dynamics
• Electronic structure

• Macroscopic
• EoS, elasticity, thermodynamics

• Data characteristics
• Three-dimensional, time-dependent
• Multivariate
• Massiveness, multiple sets
• Computational, experimental origin

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Application-Based Approach

• Numerous visualization systems exist
• None of them may be good enough
• Lack of desired functionality and flexibility

• How to meet domain-specific needs
• Presentation and interactivity
• On-the-fly data processing
• Multiple sets of data
• Visualization with database
• Remote and collaborative visualization
• Visualization/computational steering

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Current Visualization Activities

• Multiple datasets visualization (MDV)
• Electron density distribution

• Space-time multi-resolution (STMR) visualization
• Atomic structure and dynamics

• Remote visualization
• Elastic moduli and wave propagation

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Multiple Datasets Visualization Simultaneous
rendering of more than one set of data to examine
cross-correlation among them Isosurface
extraction GPU-based visualization Adaptive
scalable approach
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Example Electronic Structure
Perfect
Defect
Defect - Perfect Difference in

two images
Initial configurations
Final configurations (after relaxation)

• Mg2- vacancy defect in MgSiO3 post-perovskite

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Scalable Adaptive Isosurface Extraction

• Octree data structure

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Performance Analysis

• Performance measurement on 64 sets of scalar
  volume data with size of 2563 and 5123

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GPU-Based Visualization

• Graphics hardware assisted 3D textures
• Interactive clipping
• Isosurface

Khanduja and Karki WSCG 2005 GRAPP 2006 WSCG 2007
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Example Electronic Structure
Perfect
Defect
Defect - Perfect Difference in images
Initial configurations
Final configurations (after relaxation)

• Mg2- vacancy defect in MgSiO3 post-perovskite

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MDV Example
25 sets of the scalar volume data of 2563 size
in a planer clipped mode using 3D surface texture
mapping Electron density in liquid MgO as a
function of time Multi-scale color map Blue
0 to 0.05 Blue and green 0.05 to 0.5 Red
above 0.5
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Electron Density Defects in MgSiO3 ppv
Vacancies
Mg
Si
O
Migrating ions
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Electron Density Defects in MgSiO3 ppv
Vacancies
Mg
Si
O
Migrating ions
Spheres and lines Karki and Khanduja, EPSL, 2007
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Defects in MgSiO3 ppv Atomic Structure
Vacancies
Mg
Si
O
Migrating ions
Mg Green Si Blue O Red Vacancy site Black
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Space-Time Multiresolution (STMR) Atomistic
Visualization Integration of visualization and
complex analysis On-the-fly extraction and
rendering of a variety of data Pair correlation,
coordination and cluster structures Dynamical
behavior
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Atomistic Visualization Modules

• Approach
• Spatial proximity
• Temporal proximity
• Spatio-temporal analysis
• Model
• Complete data rendering
• Local/extracted data rendering

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Position-Time Series Data
Data P(j?t) 0 j N where P(t) pi(t)
1 i n
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Coordination Environment
Radial distribution functions
Given atomic system Hydrous MgSiO3 liquid
16 different pair correlation structures Cutoff
distances from partial RDFs
Atomic species spheres
Si-O
Coordination stability
Coordination environment
Coordination clusters
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Pair Correlation Matrix
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Radial Distribution Function
Spatial and temporal information on Si-O
coordination
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Coordination-Encoding
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Coordination Stability
Color map
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3
4
5
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The lines (thickness encoding the bond stability)
and center atoms (size encoding the coordination
stability) are color-coded to represent,
respectively, the length distribution and
coordination states. The stability represents the
fraction of the total simulation time over which
a given bond or coordination state exists.
Bhattarai and Karki, ACMSE 2007
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Stability of Different Coordination
16 coordination states
0
1
2
3
4
5
6
7
8
9
10
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Four types exist
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4
5
6
12
13
14
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Coordination Cluster
Spatial and temporal information on Si-O
coordination
The lines (thickness encoding the bond stability)
and center atoms (size encoding the coordination
stability) are color-coded to represent,
respectively, the length distribution and
coordination states. The stability represents the
fraction of the total simulation time over which
a given bond or coordination state exists.
Bhattarai and Karki, ACMSE 2007
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Coordination Cluster Per Atom
Spatial and temporal information on Si-O
coordination
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Coordination Visualization
Radial distribution functions
Si-O
Coordination stability
Coordination environment
Coordination clusters
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Visualizing Dynamics
Spheres for atomic displacements
Ellipsoids for covariance matrices

• Diffusion in 80-atoms liquid MgSiO3

Diffusion in 64-atoms liquid MgO
Bhattarai and Karki, ACMSE 2007
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Elasticity visualization Remote
execution Visualization and database
server Online data reposition
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Elasticity Visualization - ElasViz

• Multivariate elastic moduli
• Variation with pressure, temperature and
  composition

• Elastic wave propagation in an anisotropic medium
• Velocity-direction surfaces
• Anisotropic factors
• Karki and Chennamsetty, Vis. Geosci., 2004

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Modules of ElasViz
ReadData
GenerateDirection
GenerateVelocity
CijPlot
DrawVelocity
AnPlot
Other Modules
Display
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Global Visualization Mode
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Selective Visualization Mode
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Summary

• Visualization for gaining insight into a variety
  of datasets for important minerals properties and
  processes
• Increasing amounts of data from simulations and
  other resources.
• Important visualization systems under
  development
• Elasticity, atomic and electronic data
• A lot needs to be done
• Adding more functionalities
• Merging atomistic and electronic components
• Extending for remote and distributed access
• Adopting in virtual (immersive) environment.
• Support from NSF (EAR 0347204, ATM 0426601 and
  EAR 0409074).

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