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

Do we really need more data?

Data vs Knowledge

http://www.genengnews.com/

Krishna Rajan

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

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

Multidimensionality of data

Krishna Rajan

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

Ranking descriptors

Krishna Rajan

Information entropy based “phase diagrams”

Krishna Rajan

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

Developing a design rules for intermetallics

Krishna Rajan

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)

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

“Minimalism”: Linking information entropy to irreducible representations

Krishna Rajan

Linking Crystal Chemistry with Crystal Symmetry

Krishna Rajan

Limited Data Problem—no data deluge!

Data Diversity

Modeling with Data Mining

“omics” materials design Non-“omics” materials design

Accelerated Design- value of “omics” design

Krishna Rajan

2010/11- Rajan

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

Discovering Classifiers

Krishna Rajan

Ranking Descriptors

Krishna Rajan

Structure Maps from Data Mining

Krishna Rajan

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

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