A talk for the Institute of Data Analytics and High Performance Computing Chalk and Talk lunch series on Thursday April 25, 2014. This high level talk discusses materials science on the grounds of the information that drive new discoveries in materials science. Understanding the nature of the data that encompasses the landscape of materials science is important for the next generation workforce and the emerging discipline of Materials Data Scientist

1. A PRESENTATION BY TONY FAST

3. THREE-AGE SYSTEM

4. STEEL AGE

5. STEEL
ALUMINUM
NANOTECHNOLOGY
BIOMATERIALS
POLYMERS
FIBER COMPOSITES
AMORPHOUS METALS
SEMICONDUCTORS
MAGNETIC MATERIALS
CERAMICS

7. PERFORMANCE

8. METERS10-9 10-3
HIERARCHICAL NATURE OF MATERIALS
P
E
R
F
O
R
M
A

9. @BrockDavis

10. OUT-GROUP HOMOGENEITY
OBSERVED CULTURALLY

11. APPLICATION
SPACE
MATERIALS
SCIENCE

12. MATERIALS SCIENCE INFORMATION

13. PHYSICS
BASED
MODELS

14. RITCHIE GROUP, LLNL
HIGH TEMPERATURE IN SITU TENSILE
TESTING OF SiC-SiC MINICOMPOSITES

15. RITCHIE GROUP, LLNL
HIGH TEMPERATURE IN SITU TENSILE
TESTING OF C-SiC TEXTILES

16. VOORHEES GROUP, NORTHWESTERN
IN SITU VISUALIZATION OF SOLIDIFCATION INTERFACES IN AL-CU

17. STITCHED ELECTRON BACKSCATTERED DIFFRACTION
OF HEXAGONAL METALS
KALIDINDI GROUP, GATECH

18. A
T
O
M
P
R
O
B
E
M
I
C
R
S
C

19. FINITE ELEMENT CRYSTAL PLASTICITY MODELS

20. MOLECULAR DYNAMICS SIMULATIONS
OF ALUMINUM POTENTIALS

21. MOLECULAR DYNAMICS FOR POLYMERIC MATERIALS
JACOBS GROUP, GATECH

22. KALIDINDI GROUP, GATECH

23. EBSD
detector
Sample
Indenter tip
SEM pole
piece
Step 6 Step 1Step 2Step 3Step 4Step 5
KALIDINDI GROUP, GATECH
IN SITU NANOINDENTATION &
BACKSCATTERED ELECTRON
DIFFRACTION

24. MATERIALS
SCIENCE DATA
HIGH DIMENSIONAL, MULTIOMODAL, SPATIOTEMPORAL, PARTIAL DATASETS

25. THE PAST ISN”T THE FUTURE

26. β-Titanium
REDUCED OUTPUT:
Grain size
Grain Faces
Number of Grains
Mean Curvature
Nearest Grain Analysis
ROWENHORST, LEWIS, SPANOS, ACTA MAT, 2010

27. DATA SCIENCE APPLICATIONS
FOR STRUCTURAL MATERIALS

28. SCALABLE ALGORITHMS
FEATURE IDENTIFICATION
ANOMOLY DETECTION
STATISTICAL ANALYSIS
COMPUTER VISION
IMAGE SEGMENTATION
BACKGROUND REMOVAL
SIGNAL DECONVOLUTION
CLASSIFICATION
REGRESSION

29. FFT BASED METHODS FOR SPATIAL STATISTICS
A GENERALIZED FEATURE IDENTIFIER

30. FIBER SEGMENATION IN
LOW CONTRAST IMAGES

31. DIMENSION REDUCTION,
CLASSIFICATION, & COMPRESSION
HIGH DIMENSIONAL, MULTIOMODAL, SPATIOTEMPORAL, PARTIAL DATASETS

32. MODEL VERIFICATION & VALIDATION
IN MOLECULAR DYNAMICS

33. CLASSIFICATION OF PROCESSING HISTORY
IN TITANIUM ALLOYS

34. REGRESSION MODELS FOR FORWARD
MODELS & PRIOR KNOWLEDGE
Localization
Homogenization
10-9 m
10-3 m

35. FORWARD REGRESSION
MODELS FOR FUEL CELLS
MPL
GDL
Homogenization

36. FEM
ε=5e-4
LocalizationINVERTABLE MATERIALS
KNOWLEDGE SYSTEMS

37. Localization
INPUT OUTPUTControl
AnyModel
INVERTABLE MATERIALS
KNOWLEDGE SYSTEMS

38. Localization
INVERTABLE MATERIALS KNOWLEDGE
SYSTEMS FOR COMPOSITES
SCALABLE, ACCURATE, INVERTIBLE METAMODELS

39. Structure-Processing MKS
Processing History
Structure-Property
Homogenization
Structure-Property
Localization

40. “Being able to manipulate text files
at the command-line, understanding
vectorized operations, thinking
algorithmically; these are the
hacking skills that make for a
successful data hacker.”
DREW CONWAY’S PRIMARY COLORS OF DATA SCIENCE

41. TECHNICAL SKILLS
 USES DATA AS CURRENCY
 IS A SCIENTIST
NOT A PROGRAMMER
 ADDRESSES OBJECTIVES
NO PIPELINES
 USES VERSION CONTROL
 CAPABLE IN SEVERAL
PROGRAMMING
LANGUAGES
SOFT SKILLS
 SOCIAL
 INQUISITIVE
 POLYMATH
 CREATIVE PROBLEM
SOLVER

42. WHERE TO START
BY FIXING THIS