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Material Informatics: Data, Methodologies, and Applications
Informatics for the Materials Genome: a Minimalist Perspective: Krishna Rajan Iowa State University http://cosmic.mse.iastate.edu
July 13th 2011
Acknowledgements: • National Science Foundation • AFOSR
Dept. of Homeland Security • Army Research Office • Office of Naval Research • Dept. of Energy • DARPA
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Do we really need more data?
Data vs Knowledge
http://www.genengnews.com/
Krishna Rajan
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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
Krishna Rajan Krishna Rajan
High Dimensional Data
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Multidimensionality of data
Krishna Rajan
Hume-Rothery 1926, 1934
Laves 1956, 1967
Engel-Brewer 1964, 1967
Pearson 1972
Villars 1995 ……………………….
http://www.chem.ox.ac.uk/icl/heyes/structure_of_solids/Lecture3/Lec3.html#anchor5
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Multidimensionality of data
Krishna Rajan
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n
i
ii ppH1
log
Classification (partitioning of feature space)
= Minimization of information entropy
= Maximization of information gain
: Probability distribution of AB2 structure types occurred in Linus Pauling File (LPF)
Information Entropy
Krishna Rajan: ICME Symposium- MS&T
Krishna Rajan
Kong & Rajan-2012
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Ranking descriptors
Krishna Rajan
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Information entropy based “phase diagrams”
Krishna Rajan
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840 compounds
(34 structure types)
140 compounds
(14 structure types)
22 compounds
(2 structure types)
Recursive partioning to track Evolution of design rules
Tracking Structural Correlations
Krishna Rajan
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Developing a design rules for intermetallics
Krishna Rajan
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Size factor
Electrochemical factor
Valence-electron factor
GeX2
Crystal-structure design rules
Tracking design rules
Entropy scaled Structure map
Krishna Rajan
Kong & Rajan (2009/ 2012)
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The possible crystal structures of a hypothetical compound AuBe2 are suggested from the classification tree constructed by using known data.
A compound the structure is unknown
Potential structure types
Data-driven crystal chemistry (if-then) rules
Guiding Structure Prediction
Krishna Rajan
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“Minimalism”: Linking information entropy to irreducible representations
Krishna Rajan
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Linking Crystal Chemistry with Crystal Symmetry
Krishna Rajan
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Limited Data Problem—no data deluge!
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Data Diversity
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Modeling with Data Mining
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“omics” materials design Non-“omics” materials design
Accelerated Design- value of “omics” design
Krishna Rajan
2010/11- Rajan
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Data Driven Modeling
Closing the gap : developing a QSAR- a new figure of merit
Knowledge: a unified & accelerated model of materials behavior
Information: the ‘tolerance factor’ Data: developing a descriptor database
Krishna Rajan
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Discovering Classifiers
Krishna Rajan
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Ranking Descriptors
Krishna Rajan
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Structure Maps from Data Mining
Krishna Rajan
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The 4 V’s of Materials Data
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
Modeling
Krishna Rajan
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veracity variety velocity volume :Discovery Materials
Summary: “Closing the Gap”’
Experiments and physical models
Informatics, statistical learning
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)
Krishna Rajan