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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
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&THE SPACE IN BETWEEN
A PRESENTATION BY TONY FAST
STONE
AGE
IRON A
GE
COPPER
AGE
BRONZE AG
E
THREE-AGE SYSTEM
STEEL AGE
STEELALUMINUM
NANOTECHNOLOGYBIOMATERIALS
POLYMERSFIBER COMPOSITES
AMORPHOUS METALSSEMICONDUCTORS
MAGNETIC MATERIALSCERAMICS
PERFORMANCE
METERS10-9 10-3
HIERARCHICAL NATURE OF MATERIALS
PERFORMANCE
OUT-GROUP HOMOGENEITYOBSERVED CULTURALLY
APPLICATION SPACE
MATERIALSSCIENCE
MATERIALS SCIENCE INFORMATION
PHYSICSBASED MODELS
RITCHIE GROUP, LLNL
HIGH TEMPERATURE IN SITU TENSILE TESTING OF SiC-SiC MINICOMPOSITES
RITCHIE GROUP, LLNL
HIGH TEMPERATURE IN SITU TENSILE TESTING OF C-SiC TEXTILES
VOORHEES GROUP, NORTHWESTERN
IN SITU VISUALIZATION OF SOLIDIFCATION INTERFACES IN AL-CU
16 TB
STITCHED ELECTRON BACKSCATTERED DIFFRACTION OF HEXAGONAL METALS
KALIDINDI GROUP, GATECH
ATOM PROBE MICRSCOPY
FINITE ELEMENT CRYSTAL PLASTICITY MODELS
MOLECULAR DYNAMICS FOR POLYMERIC MATERIALS
JACOBS GROUP, GATECH
KALIDINDI GROUP, GATECH
EBSD detector
Sample
Indenter tip
SEM pole piece
Step 6
Step 1
Step 2
Step 3
Step 4
Step 5
KALIDINDI GROUP, GATECH
IN SITU NANOINDENTATION &BACKSCATTERED ELECTRON DIFFRACTION
MATERIALSSCIENCE DATA
HIGH DIMENSIONAL, MULTIOMODAL, SPATIOTEMPORAL, PARTIAL DATASETS
THE PAST ISN”T THE FUTURE
β-Titanium
REDUCED OUTPUT:Grain sizeGrain FacesNumber of GrainsMean CurvatureNearest Grain Analysis
ROWENHORST, LEWIS, SPANOS, ACTA MAT, 2010
DATA SCIENCE APPLICATIONSFOR STRUCTURAL MATERIALS
SCALABLE ALGORITHMS
FEATURE IDENTIFICATIONANOMOLY DETECTIONSTATISTICAL ANALYSISCOMPUTER VISIONIMAGE SEGMENTATIONBACKGROUND REMOVALSIGNAL DECONVOLUTIONCLASSIFICATIONREGRESSION
FFT BASED METHODS FOR SPATIAL STATISTICSA GENERALIZED FEATURE IDENTIFIER
FIBER SEGMENATION IN LOW CONTRAST IMAGES
DIMENSION REDUCTION, CLASSIFICATION, & COMPRESSION
HIGH DIMENSIONAL, MULTIOMODAL, SPATIOTEMPORAL, PARTIAL DATASETS
MODEL VERIFICATION & VALIDATION IN MOLECULAR DYNAMICS
CLASSIFICATION OF PROCESSING HISTORYIN TITANIUM ALLOYS
REGRESSION MODELS FOR FORWARD MODELS & PRIOR KNOWLEDGE
Localization
Homogenization
10-9 m
10-3 m
FORWARD REGRESSION MODELS FOR FUEL CELLS
MPL
GDL
Homogenization
FEMε=5e-4
LocalizationINVERTABLE MATERIALS KNOWLEDGE SYSTEMS
Localization
INPUT OUTPUTControl
Any M
odel
INVERTABLE MATERIALS KNOWLEDGE SYSTEMS
LocalizationINVERTABLE MATERIALS KNOWLEDGE SYSTEMS FOR COMPOSITES
SCALABLE, ACCURATE, INVERTIBLE METAMODELS
Structure-Processing MKS
Processing History
Structure-Property
Homogenization
Structure-Property
Localization
“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
TECHNICAL SKILLS USES DATA AS CURRENCY IS A SCIENTIST
NOT A PROGRAMMER
ADDRESSES OBJECTIVESNO PIPELINES
USES VERSION CONTROL CAPABLE IN SEVERAL
PROGRAMMING LANGUAGES
SOFT SKILLS SOCIAL INQUISITIVE POLYMATH CREATIVE PROBLEM
SOLVER
