Welcome-Introduction and Course Overview
Course Syllabus
Frequently Asked Questions
Suggested Reading
Target Audience and Recommended Background
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Accelerating Materials Development and Deployment -Importance of accelerated and integrated materials and product design/development
Why Accelerate Material Discovery and Development?
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Accelerating Materials Development and Deployment -Historical materials development and deployment cycles
Historical Materials Development Cycles
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Accelerating Materials Development and Deployment -How do we accelerate materials development and deployment?
How do we accelerate materials development and deployment
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Accelerating Materials Development and Deployment -Emergence of multi-stakeholder initiatives
Emergence of multi-stakeholder initiatives
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Accelerating Materials Development and Deployment -The Materials Innovation Ecosystem
The Materials Innovation Ecosystem
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Accelerating Materials Development and Deployment -Multiscale modeling and multilevel design of materials with structure hierarchy
Part 1:Multiscale Modeling and Multilevel Design of Materials
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Part 2: Multiscale Modeling and Multilevel design of Materials
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Accelerating Materials Development and Deployment -Decision-making in systems based robust materials design
Decision-Making in Material Design
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Accelerating Materials Development and Deployment -Multilevel decision based design
Multilevel Systems-Based Materials Design
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Earn a Georgia Tech Badge/Certificate/CEUs
Materials Knowledge and Materials Data Science -Property, structure, and process spaces
Material Property, Material Structure, and Manufacturing Processes
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Materials Knowledge and Materials Data Science -Process-Structure-Property (PSP) Linkages
Process-Structure-Property (PSP) Linkages
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Materials Knowledge and Materials Data Science -Role of Structure in PSP Linkages
Role of Structures in PSP Linkages
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Materials Knowledge and Materials Data Science -What is Data Science?
Data Science Terminology
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Materials Knowledge and Materials Data Science -The main components of Data Science
Main Components of Data Science
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Materials Knowledge and Materials Data Science -What is Big Data?
What is Big Data?
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Materials Knowledge Improvement Cycles-Digital Representation of Material Structure
Digital Representation of Material Structure
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Materials Knowledge Improvement Cycles-Spatial Correlations: n-Point Statistics
Spatial Correlations: n-Point Statistics
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Materials Knowledge Improvement Cycles-Computation and Visualization of 2-Point Spatial Correlations
Computation and Visualization of 2-Point Spatial Correlations
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Materials Knowledge Improvement Cycles-Principal Component Analyses (PCA) for low dimensional representation of material structure
Principal Component Analyses (PCA) for low dimensional representations
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Principal Component Analyses (PCA) for low dimensional representation of material structure
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Materials Knowledge Improvement Cycles-Scale-bridging: Information to Higher Length Scales (aka Homogenization)
Homogenization: Passing Information to Higher Length Scales
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Case Study in Homogenization: Plastic Properties of Two-Phase Composites-Process-Structure-Property (PSP) Linkages using Data Science Approach - Case study
Structure-Property Linkages using a Data Science Approach-Part 1
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Structure-Property Linkages using a Data Science Approach-Part 2
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Materials Innovation Cyberinfrastructure and Integrated Workflows-Materials Innovation Ecosystem
Materials Innovation Ecosystem
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Materials Innovation Cyberinfrastructure and Integrated Workflows-Materials Innovation Cyberinfrastructure
Materials Innovation Cyberinfrastucture
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Materials Innovation Cyberinfrastructure and Integrated Workflows-e-Collaboration Platforms/Environments
e-Collaboration Platforms/Environments
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Materials Innovation Cyberinfrastructure and Integrated Workflows-Materials Cyberinfrastructure - Materials Databases and Repositories
Materials Cyber-Infrastructure
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Materials Innovation Cyberinfrastructure and Integrated Workflows-PyMKS
Introduction to PyMKS Materials Knowledge Systems in Python
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Materials Data Science with PyMKS
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PyMKS website
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