Share Description
This course aims to provide a concise overview of the emerging discipline of Materials Informatics, which sits at the crossroads of materials science, computational science, and information science. This new field's attention is drawn to specific opportunities for accelerating materials development and deployment efforts. Materials with hierarchical internal structures spanning multiple length/structure scales are highlighted, as are the challenges involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (such as advanced statistics, dimensionality reduction, and metamodel formulation) and innovative cyberinfrastructure tools (such as integration platforms, databases, and customised tools for enhancing collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges.
Syllabus :
1. (A) Welcome
- What you should know before you start the course
(B) Accelerating Materials Development and Deployment
- Why Accelerate Material Discovery and Development?
- Historical Materials Development Cycles
- How do we accelerate materials development and deployment
- Emergence of multi-stakeholder initiatives
- The Materials Innovation Ecosystem
- Part 1:Multiscale Modeling and Multilevel Design of Materials
- Part 2: Multiscale Modeling and Multilevel design of Materials
- Decision-Making in Material Design
- Multilevel Systems-Based Materials Design
2. Materials Knowledge and Materials Data Science
- Material Property, Material Structure, and Manufacturing Processes
- Process-Structure-Property (PSP) Linkages
- Role of Structures in PSP Linkages
- Data Science Terminology
- Main Components of Data Science
- What is Big Data?
3. Materials Knowledge Improvement Cycles
- Digital Representation of Material Structure
- Spatial Correlations: n-Point Statistics
- Computation and Visualization of 2-Point Spatial Correlations
- Principal Component Analyses (PCA) for low dimensional representations
- Principal Component Analyses (PCA) for low dimensional representation of material structure
- Homogenization: Passing Information to Higher Length Scales
4. Case Study in Homogenization: Plastic Properties of Two-Phase Composites
- Structure-Property Linkages using a Data Science Approach-Part 1
- Structure-Property Linkages using a Data Science Approach-Part 2
5. Materials Innovation Cyberinfrastructure and Integrated Workflows
- Materials Innovation Ecosystem
- Materials Innovation Cyberinfrastucture
- e-Collaboration Platforms/Environments
- Materials Cyber-Infrastructure
- Introduction to PyMKS Materials Knowledge Systems in Python
- Materials Data Science with PyMKS
