Nanosciences

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

Nanosciences

Bill SheltonMichael O’KeefeDerrick ManciniBahram Parvin

Rick RiedelIan Anderson

March 16, 2004

Data Management workshop

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

• 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

Scientific Scope and Vision for CNMSCenter for Nanophase Materials Sciences

Nanofabrication Research Lab

CNMS Offices and Labs

The CNMS Concept:Create scientific synergies

to accelerate discoveryin nanoscale science

CNMSORNL’s

SNSCampus

JINS

SNSCLO

UnderstandingThe data chain

Data curation Data (scientific)

Data (raw)

Data (publication)

Data (metadata)Software

Hardware

Data analysis

Data visualization

Data treatment

Data diagnostics

Data acquisition

Measurement

Data simulation

Understanding

Measurement

The data chain

Data curation Data (scientific)

Data (raw)

Data (publication)

Data (metadata)Software

Hardware

Facilit

y

User

Data analysis

Data visualisation

Data treatment

Data diagnostics

Data acquisition

Data simulation

Ownership?

• 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

March 2004

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

160 Mbytes of image data per 2D reconstruction to atomic resolution.24 Gbytes per (future) 3D reconstruction to atomic resolution.

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).

Mike O’Keefe, Bahram Parvin, Larry Allard, “Structural characterization of nanoparticles”

March 2004

Atomic image simulation and comparison with observational data

Bahram Parvin, Mike O’Keefe et al, “Convergence of simulation and observational data at atomic resolution”

160 Mbytes of image data per reconstruction to atomic (sub-nanometer) resolution

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

Atomic-resolution image of carbon atoms (white) in diamond structure

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

Bahram Parvin, Mike O’Keefe et al, “Automated in-situ electron microscopy”

Below melting point

Computer-controlled tracking and shape characterization of Pb nano-particle in aluminum

Above melting point

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

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

A distributed approach?A distributed approach? ?

Data Acquisition System

Remote storage

Local users

High Speed Network

Supercomputers

Metadata

rawdata

Remote userswith local computing and storage

Remote users

Super computers Nanofabrication Research Lab

CNMS Offices and Labs

~50 TBytes/year/facility

~10 GBits/s

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

Facility

Instruments

Sample environment

Data treatment

Scientific results

Impact?

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

FacilityInstruments

Sample environment

Data treatment

Scientific results

Funding!

OAK RIDGE NATIONAL LABORATORYU. S. DEPARTMENT OF ENERGY

Oak Ridge on the NSF Teragrid