Materials Data Sciences and Informatics (Coursera)

Overview

Materials Data Sciences and Informatics 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.

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.

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

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

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

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

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

Coach

Dr. Surya Kalidindi