Materials Data Sciences and Informatics

Materials Data Sciences and Informatics

Instructor: Dr. Surya Kalidindi

Access provided by EDGE Group

19,377 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

351 reviews

Intermediate level

Some related experience required

Flexible schedule

9 hours to complete

Learn at your own pace

95%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

351 reviews

Intermediate level

Some related experience required

Flexible schedule

9 hours to complete

Learn at your own pace

95%

Most learners liked this course

Skills you'll gain

Details to know

Shareable certificate

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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 videos Total 78 minutes

Why Accelerate Material Discovery and Development? 10 minutes
Historical Materials Development Cycles 8 minutes
How do we accelerate materials development and deployment 12 minutes
Emergence of multi-stakeholder initiatives 9 minutes
The Materials Innovation Ecosystem 10 minutes
Part 1:Multiscale Modeling and Multilevel Design of Materials 9 minutes
Part 2: Multiscale Modeling and Multilevel design of Materials 6 minutes
Decision-Making in Material Design 9 minutes
Multilevel Systems-Based Materials Design 6 minutes

1 reading Total 10 minutes

Earn a Georgia Tech Badge/Certificate/CEUs 10 minutes

1 assignment Total 30 minutes

Accelerating Materials Development and Deployment 30 minutes

2 discussion prompts Total 20 minutes

Assignment #1 10 minutes
Assignment #2 10 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 videos Total 53 minutes

Material Property, Material Structure, and Manufacturing Processes 17 minutes
Process-Structure-Property (PSP) Linkages 7 minutes
Role of Structures in PSP Linkages 11 minutes
Data Science Terminology 6 minutes
Main Components of Data Science 5 minutes
What is Big Data? 7 minutes

1 assignment Total 30 minutes

Materials Knowledge and Materials Data Science 30 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 videos Total 79 minutes

Digital Representation of Material Structure 15 minutes
Spatial Correlations: n-Point Statistics 12 minutes
Computation and Visualization of 2-Point Spatial Correlations 11 minutes
Principal Component Analyses (PCA) for low dimensional representations 12 minutes
Principal Component Analyses (PCA) for low dimensional representation of material structure 13 minutes
Homogenization: Passing Information to Higher Length Scales 17 minutes

1 assignment Total 30 minutes

Materials Knowledge Improvement Cycles 30 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 videos Total 42 minutes

Materials Innovation Ecosystem 6 minutes
Materials Innovation Cyberinfrastucture 4 minutes
e-Collaboration Platforms/Environments 6 minutes
Materials Cyber-Infrastructure 4 minutes
Introduction to PyMKS Materials Knowledge Systems in Python 7 minutes
Materials Data Science with PyMKS 14 minutes

2 readings Total 20 minutes

PyMKS website 10 minutes
Take another course like this ! 10 minutes

1 assignment Total 30 minutes

Materials Innovation Cyberinfrastructure and Integrated Workflows 30 minutes

2 discussion prompts Total 20 minutes

PyMKS output 10 minutes
Auto and Crosscorrelation for a microstructure 10 minutes

Instructor

Instructor ratings

(87 ratings)

Dr. Surya Kalidindi

Georgia Institute of Technology

1 Course 19,377 learners

Offered by

Georgia Institute of Technology

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Showing 3 of 351

Reviewed on May 31, 2020

You will learn four paradigms of science: empirical, theoretical, computational, and data-driven.

Reviewed on Sep 22, 2018

Machine learning part and its application to material science was interesting but informative contents like material dev eco system and whole week 1 was more informative than logical

Reviewed on Mar 27, 2020

the course is nice and useful, but is very tough. You require a good knowledge of statistics, computation, and material science to make it through it.

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