We may earn an affiliate commission when you visit our partners.
Dr. Surya Kalidindi
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/k6onmk/materials
Here's a deal for you
Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Get offer
What's inside
Syllabus
Welcome
What you should know before you start the course
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.
Read more
Syllabus
Welcome
What you should know before you start the course
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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suitable for individuals interested in understanding the intersection of materials science, computational science, and information science
Introduces key national and international initiatives in accelerated materials discovery and development, highlighting the potential for disruptive transformation
Involves instructors who are recognized for the course's core topics
Explores the role of data science in understanding materials knowledge systems
Develops an understanding of process-structure-property linkages, emphasizing the challenges and potential solutions
Utilizes case studies to demonstrate practical applications of the concepts, enhancing understanding
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Materials informatics foundational overview
According to learners, this course offers a valuable introduction to Materials Data Sciences and Informatics, effectively bridging materials science and data science. Students find the topic highly relevant and timely, providing a solid conceptual framework and foundation for future study. The instructors are generally praised for their knowledge and clear explanations, particularly in areas like PCA. However, reviewers consistently note that the course requires significant prior knowledge in statistics, programming (like Python), and potentially advanced materials concepts, which may not be suitable for absolute beginners in these areas. Some also mention the pace can feel quite fast, and they would have preferred more practical, hands-on coding examples rather than purely theoretical coverage, suggesting it functions better as a high-level overview than a deep technical dive.
Instructors are experts and explain concepts well.
"The instructors are clearly experts in the field and explain concepts well."
"Excellent explanations, especially in modules like PCA."
"Gained a lot from the instructors' insights."
"The lectures were clear and easy to follow, thanks to the instructor."
Good introduction to key concepts and frameworks.
"Offers a great overview of the field and its potential."
"Provides a strong conceptual framework for understanding materials informatics."
"Excellent introduction to key data science concepts applied to materials."
"Gives you a high-level understanding of the concepts involved."
Covers a crucial, emerging interdisciplinary field.
"This course is highly relevant and timely for anyone in the field."
"Successfully bridges materials science and data science."
"Provides a much-needed introduction to materials informatics."
"A crucial subject for the future of materials development."
Pace can be fast; less hands-on than some hoped.
"The pace felt a bit fast at times, especially covering complex topics."
"Wish there were more hands-on coding exercises or practical examples."
"Expected more practical application rather than just theory."
"Provides an overview, but lacks deep technical or practical detail."
Requires background in stats, programming, materials.
"Requires significant background in statistics and programming (e.g., Python)."
"Not suitable for absolute beginners without prerequisites in materials science or data science."
"Need prior knowledge in materials science AND basic stats/programming."
"Some of the mathematical and statistical concepts required prior knowledge I didn't have."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Materials Data Sciences and Informatics with these
activities:
Review Linear Algebra and Statistics
Show steps
Linear algebra and statistics are essential tools in materials informatics. Reviewing these concepts will strengthen your mathematical foundation and improve your understanding of the course material.
Browse courses on
Linear Algebra
Show steps
-
Identify online resources or textbooks that cover linear algebra and statistics.
-
Review the basics of each subject.
-
Solve practice problems to test your understanding.
Review Book: Materials Science and Engineering: An Introduction
Show steps
This book provides a solid foundation in materials science, covering essential concepts and principles that will be encountered throughout the course. Reviewing it will strengthen your understanding of the subject matter.
View
Materials Science and Engineering: An...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Complete the practice problems at the end of each chapter.
-
Summarize the key concepts covered in each chapter.
Form a Study Group with Classmates
Show steps
Forming a study group with classmates will provide you with a collaborative environment to discuss course material, ask questions, and prepare for assessments.
Show steps
-
Identify classmates who are interested in forming a study group.
-
Set a regular time and place to meet.
-
Prepare an agenda for each meeting to ensure focused discussions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Process-Structure-Property (PSP) Linkages Problems
Show steps
Solving PSP linkages problems will strengthen your ability to analyze and understand the relationships between material processing, structure, and properties.
Show steps
-
Identify a simple PSP linkage problem.
-
Gather the necessary data and information.
-
Apply the appropriate analysis techniques.
-
Interpret the results and draw conclusions.
Create a Presentation on a Materials Informatics Application
Show steps
By researching and presenting a specific application of materials informatics, you will gain a deeper understanding of how this field is used in practice.
Browse courses on
Materials Informatics
Show steps
-
Choose an application of materials informatics that interests you.
-
Research the application and gather relevant information.
-
Create a presentation that clearly explains the application and its benefits.
Follow Tutorials on Using Materials Data Science Tools
Show steps
By following tutorials on using materials data science tools, you will develop practical skills in handling and analyzing materials data.
Show steps
-
Identify a reputable source for materials data science tutorials.
-
Select a tutorial that aligns with your interests and needs.
-
Follow the tutorial step-by-step and complete the exercises.
Compile and Review Course Materials
Show steps
Regularly compiling and reviewing course materials will reinforce your understanding of key concepts and help you stay organized.
Show steps
-
Gather all lecture notes, slides, and assignments.
-
Organize the materials in a logical manner.
-
Review the materials regularly to identify areas that need additional attention.
Review Linear Algebra and Statistics
Show steps
Linear algebra and statistics are essential tools in materials informatics. Reviewing these concepts will strengthen your mathematical foundation and improve your understanding of the course material.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify online resources or textbooks that cover linear algebra and statistics.
- Review the basics of each subject.
- Solve practice problems to test your understanding.
Review Book: Materials Science and Engineering: An Introduction
Show steps
This book provides a solid foundation in materials science, covering essential concepts and principles that will be encountered throughout the course. Reviewing it will strengthen your understanding of the subject matter.
View
Materials Science and Engineering: An...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Complete the practice problems at the end of each chapter.
- Summarize the key concepts covered in each chapter.
Form a Study Group with Classmates
Show steps
Forming a study group with classmates will provide you with a collaborative environment to discuss course material, ask questions, and prepare for assessments.
Show steps
Show steps
Show steps
- Identify classmates who are interested in forming a study group.
- Set a regular time and place to meet.
- Prepare an agenda for each meeting to ensure focused discussions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Process-Structure-Property (PSP) Linkages Problems
Show steps
Solving PSP linkages problems will strengthen your ability to analyze and understand the relationships between material processing, structure, and properties.
Show steps
Show steps
Show steps
- Identify a simple PSP linkage problem.
- Gather the necessary data and information.
- Apply the appropriate analysis techniques.
- Interpret the results and draw conclusions.
Create a Presentation on a Materials Informatics Application
Show steps
By researching and presenting a specific application of materials informatics, you will gain a deeper understanding of how this field is used in practice.
Browse courses on
Materials Informatics
Show steps
Show steps
Show steps
- Choose an application of materials informatics that interests you.
- Research the application and gather relevant information.
- Create a presentation that clearly explains the application and its benefits.
Follow Tutorials on Using Materials Data Science Tools
Show steps
By following tutorials on using materials data science tools, you will develop practical skills in handling and analyzing materials data.
Show steps
Show steps
Show steps
- Identify a reputable source for materials data science tutorials.
- Select a tutorial that aligns with your interests and needs.
- Follow the tutorial step-by-step and complete the exercises.
Compile and Review Course Materials
Show steps
Regularly compiling and reviewing course materials will reinforce your understanding of key concepts and help you stay organized.
Show steps
Show steps
Show steps
- Gather all lecture notes, slides, and assignments.
- Organize the materials in a logical manner.
- Review the materials regularly to identify areas that need additional attention.
Career center
Learners who complete Materials Data Sciences and Informatics will develop knowledge and skills
that may be useful to these careers:
Materials Informatics Engineer
Materials Informatics Engineer
A Materials Informatics Engineer develops and uses computational tools to analyze and interpret materials data. This course is a perfect fit for Materials Informatics Engineers as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Materials Data Scientist
Materials Data Scientist
A Materials Data Scientist collects, analyzes, and interprets materials data to identify trends and patterns. This course is a perfect fit for Materials Data Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Information Scientist
Information Scientist
An Information Scientist collects, analyzes, and interprets information to identify trends and patterns. This course may be useful for Information Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Computational Materials Scientist
Computational Materials Scientist
A Computational Materials Scientist uses computer simulations to study the properties of materials. This course may be useful for Computational Materials Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to identify trends and patterns. This course may be useful for Statisticians as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Data Scientist
Data Scientist
A Data Scientist collects, analyzes, and interprets data to identify trends and patterns. This course may be useful for Data Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines for organizations. This course may be useful for Data Engineers as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases for organizations. This course may be useful for Database Administrators as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Materials Scientist
Materials Scientist
A Materials Scientist researches, develops, and tests new materials for use in a variety of products, including electronics, aerospace components, and medical devices. This course may be useful for Materials Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve problems in business and industry. This course may be useful for Operations Research Analysts as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Materials Engineer
Materials Engineer
A Materials Engineer designs and develops new materials for use in a variety of products, including electronics, aerospace components, and medical devices. This course may be useful for Materials Engineers as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Materials Characterization Scientist
Materials Characterization Scientist
A Materials Characterization Scientist uses a variety of techniques to characterize the properties of materials. This course may be useful for Materials Characterization Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for Software Engineers as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Computer Scientist
Computer Scientist
A Computer Scientist researches, designs, and develops computer systems and applications. This course may be useful for Computer Scientists as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
Data Architect
Data Architect
A Data Architect designs and develops data architectures for organizations. This course may be useful for Data Architects as it provides an overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science.
For more career information including salaries, visit:
OpenCourser.com/course/k6onmk/materials
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Materials Data Sciences and Informatics.
Provides a broad overview of materials science and engineering, covering the basics of materials properties, processing, and applications. It would be a useful reference for students new to the field or for those looking for a refresher on the fundamentals.
Provides a practical guide to using data science techniques to solve problems in materials science. It covers topics such as data mining, machine learning, and artificial intelligence. It valuable resource for anyone interested in using data science to accelerate materials discovery and development.
Provides a comprehensive overview of the field of materials science and engineering. It covers topics such as the structure of materials, the properties of materials, and the processing of materials. It valuable resource for anyone interested in learning more about the field of materials science and engineering.
Provides a comprehensive overview of the field of materials science and engineering. It covers topics such as the structure of materials, the properties of materials, and the processing of materials. It valuable resource for anyone interested in learning more about the field of materials science and engineering.
Provides an overview of statistical methods for materials science. It covers topics such as data acquisition, processing, analysis, and modeling. It valuable resource for researchers and practitioners in this field.
Provides an overview of the field of materials science and engineering. It covers topics such as data acquisition, processing, analysis, and modeling. It valuable resource for researchers and practitioners in this field.
Provides an overview of the field of materials science and engineering. It covers topics such as data acquisition, processing, analysis, and modeling. It valuable resource for researchers and practitioners in this field.
Provides an overview of the field of materials science and engineering. It covers topics such as data acquisition, processing, analysis, and modeling. It valuable resource for researchers and practitioners in this field.
Provides an overview of the field of materials science and engineering. It covers topics such as data acquisition, processing, analysis, and modeling. It valuable resource for researchers and practitioners in this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k6onmk/materials
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Rating
4.5
Effort
Approx. 9 hours
Level
Intermediate
Via
Coursera
Institution
Georgia Institute of Technology
Instructor
Dr. Surya Kalidindi
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser