Materials Data Sciences and Informatics

About this course: This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges.

Created by:  Georgia Institute of Technology

• Taught by:  Dr. Surya Kalidindi, Professor

  The George W. Woodruff School of Mechanical Engineering

Level Intermediate Commitment 5 weeks of study, 2-3 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said 课程作业

每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。

来自同学的帮助

与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。

证书

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Georgia Institute of Technology The Georgia Institute of Technology is one of the nation's top research universities, distinguished by its commitment to improving the human condition through advanced science and technology. Georgia Tech's campus occupies 400 acres in the heart of the city of Atlanta, where more than 20,000 undergraduate and graduate students receive a focused, technologically based education.

Syllabus


WEEK 1


Welcome
What you should know before you start the course


6 readings expand

1. 阅读: Course Syllabus
2. 阅读: Frequently Asked Questions
3. 阅读: Suggested Reading
4. 阅读: Target Audience and Recommended Background
5. 阅读: Get from Georgia Tech
6. 阅读: Consent Form

Accelerating Materials Development and Deployment



• Learn and appreciate historical paradigms of advanced materials development while emphasizing the critical need for new approaches that employ data sciences and informatics as the glue to connect computational simulation and experiments to speed up the processes of materials discovery and development. • Learn about the emergence of key national and international 21st century initiatives in accelerated materials discovery and development and how they are expected to bring about a disruptive transformation of new product capabilities and time to market.


9 videos, 1 reading expand

1. Video: Why Accelerate Material Discovery and Development?
2. Video: Historical Materials Development Cycles
3. Video: How do we accelerate materials development and deployment
4. Video: Emergence of multi-stakeholder initiatives
5. Video: The Materials Innovation Ecosystem
6. Video: Part 1:Multiscale Modeling and Multilevel Design of Materials
7. Video: Part 2: Multiscale Modeling and Multilevel design of Materials
8. Video: Decision-Making in Material Design
9. Video: Multilevel Systems-Based Materials Design
10. 讨论提示: Assignment #1
11. 讨论提示: Assignment #2
12. 阅读: Earn a Georgia Tech Badge/Certificate/CEUs

Graded: Accelerating Materials Development and Deployment

WEEK 2


Materials Knowledge and Materials Data Science



• Understand property, structure and process spaces • Learn about Process-Structure-Property Linkages • Learn what does Materials Knowledge mean • Learn about a role of Data Science in Materials Knowledge System • Overview approaches and main components of Data Science • Learn about a new discipline - Materials Data Sciences


6 videos expand

1. Video: Material Property, Material Structure, and Manufacturing Processes
2. Video: Process-Structure-Property (PSP) Linkages
3. Video: Role of Structures in PSP Linkages
4. Video: Data Science Terminology
5. Video: Main Components of Data Science
6. Video: What is Big Data?

Graded: Materials Knowledge and Materials Data Science

WEEK 3


Materials Knowledge Improvement Cycles
• Learn material structure and its digital representation • Learn how to calculate 2-point statistics • Learn how Principal Component Analysis can be used to reduce dimensionality • Understand Homogenization and Localization concepts


6 videos expand

1. Video: Digital Representation of Material Structure
2. Video: Spatial Correlations: n-Point Statistics
3. Video: Computation and Visualization of 2-Point Spatial Correlations
4. Video: Principal Component Analyses (PCA) for low dimensional representations
5. Video: Principal Component Analyses (PCA) for low dimensional representation of material structure
6. Video: Homogenization: Passing Information to Higher Length Scales

Graded: Materials Knowledge Improvement Cycles

WEEK 4


Case Study in Homogenization: Plastic Properties of Two-Phase Composites
This module demonstrates a homogenization problem based on an example of two-phase composites


2 videos expand

1. Video: Structure-Property Linkages using a Data Science Approach-Part 1
2. Video: Structure-Property Linkages using a Data Science Approach-Part 2

Graded: Case Study in Homogenization: Plastic Properties of Two-Phase Composites

WEEK 5


Materials Innovation Cyberinfrastructure and Integrated Workflows



• Learn about materials innovation system and cyberinfrastructure • Review Materials Databases, e-collaboration platforms and code repositories • Learn why integrated workflows are needed • Define Metadata, Structured and Unstructured data • Learn about available services for e-collaborations


6 videos, 2 readings expand

1. Video: Materials Innovation Ecosystem
2. Video: Materials Innovation Cyberinfrastucture
3. Video: e-Collaboration Platforms/Environments
4. Video: Materials Cyber-Infrastructure
5. Video: Introduction to PyMKS Materials Knowledge Systems in Python
6. Video: Materials Data Science with PyMKS
7. 阅读: PyMKS website
8. 讨论提示: PyMKS output
9. 讨论提示: Auto and Crosscorrelation for a microstructure
10. 阅读: Take another course like this !

Graded: Materials Innovation Cyberinfrastructure and Integrated Workflows