Data Management workshop
Nanosciences
Bill Shelton
Michael O’Keefe
Derrick Mancini
Bahram Parvin
Rick Riedel
Ian Anderson
March 16, 2004
OAK RIDGE NATIONAL LABORATORY
U. S. DEPARTMENT OF ENERGY
Scientific Scope and Vision for CNMS
Center for Nanophase Materials Sciences
•
A highly collaborative and
multidisciplinary research center
•
Co-located with the Spallation Neutron
Source (SNS) and the Joint Institute
for Neutron Sciences (JINS) on
ORNL’s “new campus”
•
JINS: Housing and dining facilities,
auditorium, classrooms, for research
visitors and students
•
•
SNS: Will provide access to unique
neutron scattering capabilities for
nanoscience
CNMS: Provides urgently needed
capabilities for materials synthesis,
nanofabrication, and modeling
The CNMS Concept:
Create scientific synergies
to accelerate discovery
nanoscale science
OAK Rin
IDGE NATIONAL LABORATORY
U. S. DEPARTMENT OF ENERGY
Nanofabrication Research Lab
CNMS Offices and Labs
ORNL’s
SNS
Campus
CNMS
SNS
CLO
JINS
Understanding
The data chain
Data (publication)
Data simulation
Data curation
Data (scientific)
Data analysis
Data visualization
Data treatment
Data (raw)
Data diagnostics
Data (metadata)
Software
Data acquisition
Measurement
Hardware
Understanding
The data chain
Data (publication)
Data simulation
Data curation
Data (scientific)
Data analysis
Ownership?
Data visualisation
Data treatment
Data (raw)
Data diagnostics
Data (metadata)
Software
Data acquisition
Measurement
Hardware
•
Data and Databases
•
Metadata and Data Curation
•
Data Visualization
•
Remote Collaboration and Remote Access
•
Automation and Intelligent Control
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Simulation (‘in silico’ experimentation)
•
Distributed Computing (Grids)
•
Synergy
Motivation
• Management and computational requirements of
nano-science data are complex
— Three dimensional structures represented at
nano (shape level) and sub-nano (atomic level)
— Flexible topologies as a function of external
stress and atomic interactions (temporal
evolution)
— Presence of real data for validation and
refinement of the model parameters
— Multi-resolution information from sub-nanometer
to micro-meter, computed quantitative data, meta
data
— Variable data formats
March 2004
Atomic image reconstruction from
observational data
Image of 7nm Au nanoparticle
supported on carbon substrate.
Reconstructed to subnanometer resolution from 20
electron microscope images.
Columns of atoms viewed endon (white dots) reveal the
internal structure. The particle
exhibits 5-fold twinning, with
one twin disordered to take up
strain (right).
160 Mbytes of image data per 2D reconstruction to atomic resolution.
24 Gbytes per (future) 3D reconstruction to atomic resolution.
Mike O’Keefe, Bahram Parvin, Larry Allard, “Structural characterization of nanoparticles”
March 2004
Atomic image simulation and
comparison with observational data
160 Mbytes of image data per reconstruction to atomic
(sub-nanometer) resolution
Atomic-resolution image of carbon
atoms (white) in diamond structure
On-line image simulated from atomic model
available to operator at the microscope
Drag and drop capability for validation
of experimental image with simulated
“Virtual Electron Microscope” image
Bahram Parvin, Mike O’Keefe et al, “Convergence of simulation and observational data at
atomic resolution”
March 2004
Shape evolution at nano-scale
• Macro-level shape representation as a function of stress
(1.5 Gbytes/10-minute experiment)
— Automated tracking of nano-particles
— Managing images, quantified nano-particle shape
representation, and time-varying stress data
— Kinetics of macro-level shape
• Comparison to simulated models
Below melting point
Above melting point
Computer-controlled tracking and shape characterization of Pb nano-particle in aluminum
Bahram Parvin, Mike O’Keefe et al, “Automated in-situ electron microscopy”
March 2004
Issues on shape reconstruction and
comparison at nano-scale
• 3D Reconstruction from sparse views (1 - 2 Gbytes/reconstruction)
• 3D Geometric representation and comparison
• Tracking computed geometries from macro to sub-nano-scale
Ge Cong and Bahram Parvin, “Shape from Equal Thickness Contours”, 2001
March 2004
Challenges
• Tracking three dimensional shape evolution of the
range from macro to nano-scale
• Developing object level multi-scale representation of
shape features for querying and comparative
analysis
• Migrating toward structure-function informatics
instead of more low-level-representation data
management...
• Rapid simulation tsimulation << tmeasurement
• Intelligent Control
• Synergy
March 2004
?
A distributed approach?
Super computers
Nanofabrication Research Lab
CNMS Offices and Labs
Data
Acquisition
System
raw
data
~50 TBytes/year/facility
Remote users
with local computing
and storage
Local
users
Metadata
High Speed
Network
~10 GBits/s
OAK RIDGE NATIONAL LABORATORY
Supercomputers
U. S. DEPARTMENT OF ENERGY
Remote storage
Remote
users
Impact?
Facility
Instruments
Sample environment
Data treatment
Scientific results
OAK RIDGE NATIONAL LABORATORY
U. S. DEPARTMENT OF ENERGY
Funding!
Facility
Instruments
Sample environment
Data treatment
Scientific results
OAK RIDGE NATIONAL LABORATORY
U. S. DEPARTMENT OF ENERGY
Oak Ridge on the NSF Teragrid
OAK RIDGE NATIONAL LABORATORY
U. S. DEPARTMENT OF ENERGY