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Nanosciences

Data Management workshop. Nanosciences. Bill Shelton Michael O’Keefe Derrick Mancini Bahram Parvin Rick Riedel Ian Anderson. March 16, 2004. Nanofabrication Research Lab. CNMS Offices and Labs. ORNL’s SNS Campus. CNMS. SNS CLO. JINS.

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Nanosciences

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  1. Data Management workshop Nanosciences Bill Shelton Michael O’Keefe Derrick Mancini Bahram Parvin Rick Riedel Ian Anderson March 16, 2004

  2. Nanofabrication Research Lab CNMS Offices and Labs ORNL’s SNS Campus CNMS SNS CLO JINS Scientific Scope and Vision for CNMSCenter 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 in nanoscale science

  3. Data (publication) Data simulation Data (scientific) Data analysis Data visualization Data treatment Data (raw) Data diagnostics Data (metadata) Software Data acquisition Hardware Measurement Understanding The data chain Data curation

  4. Data (publication) Data simulation Data (scientific) Data analysis Data visualisation Data treatment Data (raw) Data diagnostics Data (metadata) Software Data acquisition Hardware Understanding The data chain User Data curation Ownership? Facility Measurement

  5. Data and Databases • Metadata and Data Curation • Data Visualization • Remote Collaboration and Remote Access • Automation and Intelligent Control • Simulation (‘in silico’ experimentation) • Distributed Computing (Grids) • Synergy

  6. 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

  7. Atomic image reconstruction from observational data Image of 7nm Au nanoparticle supported on carbon substrate. Reconstructed to sub-nanometer resolution from 20 electron microscope images. Columns of atoms viewed end-on (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”

  8. 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 Drag and drop capability for validation of experimental image with simulated “Virtual Electron Microscope” image On-line image simulated from atomic model available to operator at the microscope Bahram Parvin, Mike O’Keefe et al, “Convergence of simulation and observational data at atomic resolution”

  9. 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”

  10. Ge Cong and Bahram Parvin, “Shape from Equal Thickness Contours”, 2001 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

  11. 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

  12. ? Nanofabrication Research Lab CNMS Offices and Labs Remote users with local computing and storage A distributed approach? Super computers Data Acquisition System ~50 TBytes/year/facility Local users raw data High Speed Network Remote users Metadata ~10 GBits/s Remote storage Supercomputers

  13. Facility Instruments Sample environment Data treatment Scientific results Impact?

  14. Facility Instruments Sample environment Funding!

  15. Oak Ridge on the NSF Teragrid

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