About 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.
Taught by: Dr. Surya Kalidindi, Professor
The George W. Woodruff School of Mechanical Engineering
| Level | Intermediate |
| Commitment | 5 weeks of study, 2-3 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Leyendo: Course Syllabus
- Leyendo: Frequently Asked Questions
- Leyendo: Suggested Reading
- Leyendo: Target Audience and Recommended Background
- Leyendo: Get More from Georgia Tech
- Leyendo: Consent Form
- Video: Why Accelerate Material Discovery and Development?
- Video: Historical Materials Development Cycles
- Video: How do we accelerate materials development and deployment
- Video: Emergence of multi-stakeholder initiatives
- Video: The Materials Innovation Ecosystem
- Video: Part 1:Multiscale Modeling and Multilevel Design of Materials
- Video: Part 2: Multiscale Modeling and Multilevel design of Materials
- Video: Decision-Making in Material Design
- Video: Multilevel Systems-Based Materials Design
- Cuadro de aviso de la discusión: Assignment #1
- Cuadro de aviso de la discusión: Assignment #2
- Leyendo: Earn a Georgia Tech Badge/Certificate/CEUs
- Video: Material Property, Material Structure, and Manufacturing Processes
- Video: Process-Structure-Property (PSP) Linkages
- Video: Role of Structures in PSP Linkages
- Video: Data Science Terminology
- Video: Main Components of Data Science
- Video: What is Big Data?
- Video: Digital Representation of Material Structure
- Video: Spatial Correlations: n-Point Statistics
- Video: Computation and Visualization of 2-Point Spatial Correlations
- Video: Principal Component Analyses (PCA) for low dimensional representations
- Video: Principal Component Analyses (PCA) for low dimensional representation of material structure
- Video: Homogenization: Passing Information to Higher Length Scales
- Video: Structure-Property Linkages using a Data Science Approach-Part 1
- Video: Structure-Property Linkages using a Data Science Approach-Part 2
- Video: Materials Innovation Ecosystem
- Video: Materials Innovation Cyberinfrastucture
- Video: e-Collaboration Platforms/Environments
- Video: Materials Cyber-Infrastructure
- Video: Introduction to PyMKS Materials Knowledge Systems in Python
- Video: Materials Data Science with PyMKS
- Leyendo: PyMKS website
- Cuadro de aviso de la discusión: PyMKS output
- Cuadro de aviso de la discusión: Auto and Crosscorrelation for a microstructure
- Leyendo: Take another course like this !
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
This is a great starter course for materials informatics. It covers a good amount of topics and uses a nice case study to reinforce digital representation of data, spatial correlations, principal component analysis, and regression. I really liked the examples of pyMKS. My only suggestions is it would have been nice to have more hands-ons use of pyMKS and sci-kit learn. This could have been accomplished through a course project or homeworks.
very beneficial
Great, fantastic information that made me see the importance of data sciences in materials science and engineering. My only request would be to potentially spend more time fleshing out PCA and the statistical tools around it; most of it went over my head without seeing a step-by-step application of it that showed the calculations. Maybe it could be optional so that those who are already strong in PCA can skip it.
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Thank you for the course. It is very helpful for my deeper understanding of Materials Informatics. I hope I can get more knowledge and assistance from Professors for my research in this field in future. Thank you!