Improvements in algorithms, technology, and computation are directly impacting the landscape of information use in materials science. The 3 V’s of Big Data (volume, velocity, and variety) are becoming evermore apparent within all sectors of the field. Novel approaches will be required to confront the emerging data deluge and extract the richest knowledge from simulated and empirical information in complex evolving 3-D spaces. Microstructure Informatics (μInformatics) is an emerging suite of signal processing techniques, advanced statistical tools, and data science methods tailored specifically for this new frontier. μInformatics curates and transforms large collections of materials science information using efficient workflows to extract knowledge of bi-directional structure-property/processing connections for most material classes. In this talk, a few early case studies in data-driven methods to solve materials science problems will be explored. Emerging spatial statistics tools will be explored that enable an objective comparison of static and evolving 3-D material volumes from molecular dynamics simulation, micro-CT, and Scanning Electron Microscopy. Also, the statistics will provide a foundation to create improved bottom-up homogenization relationships in fuel cell materials. Lastly, applications of the Materials Knowledge System, a data-driven meta-model to create top-down localization relationships will be explored for phase field model and finite element model information.

1. Data Science Solutions by Materials Scientists
The Early Case Studies
Tony Fast
Materials Data Analyst
Materials Informatics for
Engineering Design
Woodruff School of
Mechanical Engineering
Georgia Institute
of Technology
*Any MINED shield is a link to a resource.

2. An Archival and Self Describing Data Format using HDF5
Data and Metadata stored in one file, Support in many languages, and Ideal
support for high-dimensional data
*MXADataModel – Archival Data Format – ONR/DARPA Dynamic 3-D Digital Structures Program

3. HDF5 - The little zip file that could…
One Dataset – 1.6GB – 4 Experiments –with 160 Datasets each
…..no long term value.

4. Volume Variety Velocity = Big Data
Materials Science
Polymer - MD
Titanium
Jacobs -GaTech
Frasier -OSU
Martensitic Steel
Gumbusch
SiC/SiC
Ritchie- LLNL
Bamboo
Wegst - Dartmouth
Al-Cu Solidification
Voorhees - NW
The velocity that data is generated will rise and the
speed that it will be analyzed in will decrease.

5. β-Titanium
REDUCED OUTPUT:
Grain size
Grain Faces
Number of Grains
Mean Curvature
Nearest Grain Analysis
10 micron resolution with 4300 Grains
Compare with empirical models
Materials Science is a Big Data domain, but it is not treated that way.
Rowenhorst, Lewis, Spanos, Acta Mat, 2010

6. Harvard Clean Energy
Project Database
AFLOW, Curtarolo Group
Example Databases

7. STRUCTURE
INFORMATICS
WORKFLOW
INTELLIGENT DESIGN OF
EXPERIMENTS
PHYSICS BASED MODELS
SIMULATION EXPERIMENT
MICROSTRUCTURE (MATERIAL)
SIGNAL PROCESSING
ADVANCED & OBJECTIVE
STATISTICAL ENCODING
DATA SCIENCE MODULES
INNOVATION ACCOUNTING
Microstructure Informatics is a scalable,
data-driven system to mine structureproperty/processing connections from
experimental and simulation materials
science information; structure being the
independent variable. The system is
agnostic to material system and length
scale, objectively quantifiable, and
rapidly iterates in less cycles for both
materials improvement and discovery.

8. DATA SCIENCE
MODULES
Property
Microstructure
Material Structure
Processing
Data science modules are machine learning and statistical
tools
to
extract
rich
bi-directional
structureproperty/processing linkages from encodings of materials &
microstructure datasets. Mining modules create structure
taxonomies, homogenization and localization relationships,
ground truth comparison between simulation and experiment,
materials discovery, and materials improvement.

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

10. SPATIAL
STATISTICS
Statistical correlations between random points in space/time which
reveal systematic patterns in the microstructure. Contains the original
μS within a translation & inversion. An objective encoding for most
materials datasets.
t
h'
msh ms+t
ft hh' = å
Dt
s
t
t

11. The fidelity of the spatial statistics are impacted by how the
material structure is parameterized as a signal.
CURRENT APPLICATIONS
metals, polymers, fuel cells, cmc, md, & a bunch of other things
TYPES OF SIGNALS
sparse, experimental, simulation, heterogeneous, surface, bulk

12. Objective Microstructure Classification of α-β Titanium
Images StatisticsMine with Principal Component Analysis

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

14. Bottom-up Homogenization Relationships
model
GDL
MPL
simulation
X-CTFinite Element ModelingStatistics
Regression to connect the statistics with diffusivity values from FEM

15. Meta-modeling with Materials Knowledge Systems
Top-down localization relationships
FEM
ε=5e-4
ps = åå a m
h
t
t
h
s+t
h
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.

16. Meta-modeling with Materials Knowledge Systems
Any Model
Top-down localization relationships
INPUT
OUTPUT
Control
ps = åå a m
h
t
t
h
s+t
h
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.

17. 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.
OTHER APPLICATIONS
Spinodal Decomposition, Grain Coarsening,
Thermo-mechanical, Polycrystalline

18. Objective parametric descriptors and data science enable integration
of bi-direction structure-property/processing linkages.
Structure-Property
Homogenization
Structure-Processing MKS
Processing History
Structure-Property
Localization

19. Data enables bidirectional S-P/P, multiscale integration, and higher throughput
CORE TECHNOLOGIES TO FUEL THE DATA AGE OF MATERIALS SCIENCE
Open Access, Open Source Software, Scalable Databases, High-Statistical
Throughput Simulation and Experiment, Image Segmentation, Machine
Learning, Scalable Databases, Metadata Integration, Mobile Technology,
Visualization, High Performance Computing,
Cyberinfrastructure/Collaboratories, Collaboration & Sharing

20. Selected Links
Any shield in this presentation is a link
HDF5
HDFView
MXADataModel
Curtarolo Group
AFLOW
Harvard Clean Energy Project
Serial Sectioned Titanium
MATIN
Materials Genome Initiative
http://www.hdfgroup.org/HDF5/whatishdf5.html
http://www.hdfgroup.org/hdf-java-html/hdfview/
http://mxa.web.cmu.edu/Background.html
http://www.mems.duke.edu/faculty/stefano-curtarolo
http://materials.duke.edu/apool.html
http://www.molecularspace.org/
https://cosmicweb.mse.iastate.edu/wiki/pages/viewpage.action?pageId=753830
http://www.materials.gatech.edu/matin
http://www.whitehouse.gov/mgi