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.
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There are 6 modules in this course
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
• 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
• 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
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
Instructor
Offered by
Recommended if you're interested in Chemistry
University of California, Davis
EIT Digital
Ball State University
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Reviewed on May 31, 2020
It includes ausam information in structured manner to learn the subject easily.
Reviewed on May 10, 2020
A perfect course if one wants to pursue a research career in material science with an engineering background.
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
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