A presentation given at Novelis R&D in Kennesaw,Ga on Wednesday August 28 2013. The presentation was organized by Babak Raeisinia. The presentation provides a scope of what emerging information science, data science, and microstructure informatics techniques can used to drive the Materials Genome Initiative.

1. Information Sciences to Fuel the
Data Age of Materials Science
Tony Fast
Materials Data Analyst
MINED
Materials Informatics for Engineering Design
Georgia Institute of Technology

2. MATERIALS SCIENCE IS BIG DATA
Ad Hoc Standards, Silos, Integration, Software,
Equipment, Data Formats, Ideas

3. The Materials Innovation Network is a collaborative environment built to fuel
the Materials Genome Initiative by managing users and their digital data for
microstructure driven materials development and improvement.
SOCIAL NETWORK Manages users, projects, and expert communities engaged
in materials science related efforts. A melting pot for materials scientists, big
data, and integration.
CODE REPOSITORY A platform with an embedded versioning system to
develop codes and deployable tools for the MATIN community at large. This
platform will enable good coding practices and rapid delivery of academic
utilities to market quickly.
DATABASE The database is the unifying feature of MATIN. This graph
database is specifically designed to store nearly all types of materials datasets,
maintain data provenance, semantically query metadata, learn design patterns
( or workflows ), support the big data generation, and establish a federated
database with access control for academia, industry, and national labs.

4. Database
Information
ModelsAnalytics
CodesUsers
Social Network
Versioning
Control
Database API
Collaborative Workspace
TEAM 1
Information / Code
Upload
Query/Download
Analytics
Model Development
DataProvenance New Data
Object
Workflow
Data Object
Metadata
Collaborative Workspace
TEAM 2
Information / Code
Upload
Query/Download
Analytics
Model Development
Metadata
MATIN will enable
• Collaboration
• Standarization
• Electronic
Recording
• Data Management
and Federated
• High Value Testing
• Knowledge
Transfer

5. STRUCTURE
INFORMATICS
WORKFLOW
PHYSICS BASED MODELS
SIMULATION EXPERIMENT
MICROSTRUCTURE (MATERIAL)
SIGNAL MODULES
ADVANCED & OBJECTIVE
STATISTICAL MODULES
DATA MINING MODULES
VALUE ASSESSMENT
INTELLIGENTDESIGNOF
EXPERIMENTS
Microstructure Informatics is a data-
driven system to mine structure-
property/processing connections from
experimental and simulation materials
science information. The system is
agnostic to material system and length
scale, objectively quantifiable, and
rapidly iterates in less cycles for both
materials improvement and discovery.

6. Microstructure signal modules are (semi) automated tools
to identify local and effective microstructure features.
Aluminum in Epoxy Titanium
EMMPM - BlueQuartz
Bamboo
Martensitic Steel SiC/SiC Al-Cu Solidification

7. ADVANCED & OBJECTIVE
STATISTICAL MODULES
THE MICROSTRUCTURE IS A SAMPLE
IN AN IMMENSE STATISTICAL POPULATION.
α-β Titanium

8. SPATIAL
STATISTICS
ft
hh'
=
ms
h
ms+t
h'
Dts
∑t t
t
Statistical correlations between random points in space/time which
reveal systematic patterns in the microstructure. Contains the original
µS within a translation & inversion.

9. CURRENT APPLICATIONS
metals, polymers, fuel cells, cmc, md, & a bunch of other things
TYPES OF SIGNALS
sparse, experimental, simulation, heterogeneous, surface, bulk

10. DATA MINING
MODULES
Microstructure
Material
ProcessingProperty
Mining modules are machine Learning solutions to extract
rich bi-directional structure-property/processing linkages from
materials & microstructure datasets. Mining modules create
structure taxonomies, homogenization and localization
relationships, ground truth comparison between simulation
and experiment, materials discovery, and materials
improvement.

11. Objective Microstructure Classification of α-β Titanium
Images àStatisticsààMine with Principal Component Analysis

12. Mechanical Deformation
of Polymer Chains
Molecular Dynamics
of Aluminum Atoms

13. MPL
GDL
X-CTàFinite Element ModelingàStatisticsàà
Regression to connect the statistics with diffusivity values from FEM
Bottom-up Homogenization Relationships

14. FEM"
ε=5e-4"
Meta-modeling with Materials Knowledge Systems
Top-down localization relationships
ps = at
h
ms+t
h
h
∑
t
∑
The MKS design filters that capture the effect of the local arrangement of
the microstructure on the response. The filters are learned from physics
based models and can only be as accurate as the model never better.

15. OTHER APPLICATIONS"
Spinodal Decomposition, Grain Coarsening, Thermomechanical, Polycrystalline
Top-Down Localization Relationships for High Contrast Composites
The MKS is a scalable, parallel meta-model that learns from physics based
models to enable rapid simulation at a cost in accuracy.
N2 vs. Nlog(N) complexity
It learns top-down localization relationships to extra extreme value events and
enables multiscale integration.

16. Structure-Processing MKS
Processing History
Structure-Property
Homogenization
Structure-Property
Localization
Illustrating Integration

17. Data enables bidirectional S-P/P, multiscale integration, and higher throughput
OTHER DOMAINS THAT WILL FUEL BIG DATA IN MATERIALS SCIENCE
information gain theory, digital signal processing, regressions, statistics,
high performance computing, cloud computing, databases, mobile devices,
a connected community