transforming data to knowledge for enabling … the materials genome: transforming data to knowledge...
TRANSCRIPT
Mapping the materials genome:
transforming data to knowledge for enabling
Materials-by-Design
Krishna Rajan Wilkinson Professor of Interdisciplinary Engineering Iowa State University
Brown University: March 29th 2012
Do we really need more data? Is informatics the same as managing databases? Is modeling the same as integrating computational materials engineering? Is high throughput data collection or computation the same as high throughput
scientific discovery?
Outline: Data Driven Materials-by-Design
Challenging the existing (affirmative) paradigms for materials-by design
Krishna Rajan: Iowa State University
Developing a “genomics” framework for materials-by design
Classifying materials design problems in terms of data characteristics: Volume, Velocity, Variety, Veracity (courtesy L. Haas –IBM)
Differentiating Data from Knowledge : Materials science examples New paradigms for information management
systems for the Materials Genome: Materials cartography Materials barcode
http://www.genengnews.com/
• Need to establish a genomics-proteomics-metabolomics etc. :Systems Biology approach
• What are the data management and sharing issues for a systems approach?
Data and Materials Genome
Krishna Rajan: Iowa State University
Functionality = F ( x1 , x2 , x3 , x4 , x5 , x6 , x7 , x8 ……)
Issues: • how many variables? • which variables are important? • classify behavior among variables • making quantitative predictions …relate functionality to variables …
• traditionally we describe them by empirical equations: •Quantitative Structure Activity Relationships (QSARs) are derived from data mining techniques not assuming a priori which physics is the most important
Need to build database with these variables
The 4 V’s of Materials Data: status report
VARIETY: data in many forms
Engineering design / materials insertion
Multiscale data
VOLUME: data at rest
Materials reference data Thermodynamic Crystallography Property
VELOCITY: data in motion Materials Characterization
in-situ materials dynamics ( x-ray, ..)
Time-of-flight data
VERACITY: data in doubt
Incomplete data, ambiguities, missing
data
Phase diagrams, Property maps
Krishna Rajan: Iowa State University
Modeling
http://iusti.polytech.univ-mrs.fr/MOLTEN_SALTS/
Data Volume : discovery from static data
Krishna Rajan: Iowa State University
Accelerated reference data discovery
Outlier detection
Classification detection
Krishna Rajan
Data Volume : discovery from static data: …discovering need for new data
Informatics Based Optimization of Crystallographic Descriptors for Framework Structures; A. Rajagopalan and K. Rajan in Combinatorial and High-Throughput Discovery and Optimization of Catalysts and Materials Editors: W.Maier and R. A. Potyrailo ( CRC press – 2005)
Statistical removing of outliers: • better models from same data •new design rationale for metal oxide frameworks
Krishna Rajan: Iowa State University
Data Volume: too much data?......Complexity – Uncertainty tradeoff
Petterson et.al. Mater. Manuf. Proc. (2009)
# networks
Predator-Prey models
Krishna Rajan: Iowa State University
Data Velocity: discovering catalyst chemistries
Maier- Saarbruken J. Comb. Chem., 2009, 11 (3), pp 385–392
Chemical bonding/molecular information
Elemental information
Imaging and structural information
Data Velocity: pushing materials characterization techniques to limits
Interrogation and analysis of fast and large data sets via scientific visualization
Bryden et.al (2012) Suram and Rajan (2009,2012)
Krishna Rajan: Iowa State University
Data Variety: data driven design of drug delivery polymers
Ideal vaccine will mimic the way in which a naturally occurring infection induces a robust immune response yet avoid the undesirable effects of disease
Ulery et.al (2012)
Krishna Rajan: Iowa State University
Data Veracity: shrinking the data ‘gap” with sparse, skewed and unscaled data
Closing the gap : developing a QSAR- a new figure of merit
Knowledge: a unified model of materials behavior
Information: the ‘tolerance factor’
Utility: accelerated discovery
Data: developing a descriptor database
Krishna Rajan: Iowa State University
Materials Cartography: information theoretic data system mapping data flow
DATA POINTS Phone-Call Cartography- CARLO RATTI
Kong and Rajan (2012)
Krishna Rajan: Iowa State University
Materials Barcode: a new genre of materials data libraries Crystal chemistry barcodes
Density of states barcode Combinatorial experimental barcodes
J. Comb. Chem., 2009, 11 (3), pp 385–392
Krishna Rajan: Iowa State University
veracity variety velocity volume :Designby Materials
Summary: “Closing the Gap” for Materials-by-Design…….harnessing the 4 ‘V’s’
Experiments and physical models
Informatics, statistical learning
Krishna Rajan: Iowa State University
To transform the “Materials Genome” from a concept to reality we need an information system that can enable and accelerate the Data to Knowledge transformation (the new paradigm for Materials-
by-Design) •Materials Barcode Project •Materials Cartography Atlas