Materials Data Sciences and Informatics

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Materials Data Sciences and Informatics

Instructor: Dr. Surya Kalidindi

19,605 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Collaborative Software

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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There are 6 modules in 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.

What you should know before you start the course

What's included

6 readings

• 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.

What's included

9 videos1 reading1 assignment2 discussion prompts

9 videosTotal 78 minutes

Why Accelerate Material Discovery and Development?10 minutes
Historical Materials Development Cycles8 minutes
How do we accelerate materials development and deployment12 minutes
Emergence of multi-stakeholder initiatives9 minutes
The Materials Innovation Ecosystem10 minutes
Part 1:Multiscale Modeling and Multilevel Design of Materials9 minutes
Part 2: Multiscale Modeling and Multilevel design of Materials6 minutes
Decision-Making in Material Design9 minutes
Multilevel Systems-Based Materials Design6 minutes

1 readingTotal 10 minutes

Earn a Georgia Tech Badge/Certificate/CEUs10 minutes

1 assignmentTotal 30 minutes

Accelerating Materials Development and Deployment30 minutes

2 discussion promptsTotal 20 minutes

Assignment #110 minutes
Assignment #210 minutes

• 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

What's included

6 videos1 assignment

6 videosTotal 53 minutes

Material Property, Material Structure, and Manufacturing Processes17 minutes
Process-Structure-Property (PSP) Linkages7 minutes
Role of Structures in PSP Linkages11 minutes
Data Science Terminology6 minutes
Main Components of Data Science5 minutes
What is Big Data?7 minutes

1 assignmentTotal 30 minutes

Materials Knowledge and Materials Data Science30 minutes

• 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

What's included

6 videos1 assignment

6 videosTotal 79 minutes

Digital Representation of Material Structure15 minutes
Spatial Correlations: n-Point Statistics12 minutes
Computation and Visualization of 2-Point Spatial Correlations11 minutes
Principal Component Analyses (PCA) for low dimensional representations12 minutes
Principal Component Analyses (PCA) for low dimensional representation of material structure13 minutes
Homogenization: Passing Information to Higher Length Scales17 minutes

1 assignmentTotal 30 minutes

Materials Knowledge Improvement Cycles30 minutes

This module demonstrates a homogenization problem based on an example of two-phase composites

What's included

2 videos1 assignment

• 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

What's included

6 videos2 readings1 assignment2 discussion prompts

6 videosTotal 42 minutes

Materials Innovation Ecosystem6 minutes
Materials Innovation Cyberinfrastucture4 minutes
e-Collaboration Platforms/Environments6 minutes
Materials Cyber-Infrastructure4 minutes
Introduction to PyMKS Materials Knowledge Systems in Python7 minutes
Materials Data Science with PyMKS14 minutes

2 readingsTotal 20 minutes

PyMKS website10 minutes
Take another course like this !10 minutes

1 assignmentTotal 30 minutes

Materials Innovation Cyberinfrastructure and Integrated Workflows30 minutes

2 discussion promptsTotal 20 minutes

PyMKS output10 minutes
Auto and Crosscorrelation for a microstructure10 minutes

Instructor

Dr. Surya Kalidindi

Georgia Institute of Technology

1 Course19,605 learners

Offered by

Georgia Institute of Technology

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