Informações sobre o curso: 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.
Ministrado por: Dr. Surya Kalidindi, Professor
The George W. Woodruff School of Mechanical Engineering
| Nível | Intermediate |
| Compromisso | 5 semanas de estudo,2-3 horas/semana |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Reading: Course Syllabus
- Reading: Frequently Asked Questions
- Reading: Suggested Reading
- Reading: Target Audience and Recommended Background
- Reading: Get More from Georgia Tech
- Reading: Consent Form
- Vídeo: Why Accelerate Material Discovery and Development?
- Vídeo: Historical Materials Development Cycles
- Vídeo: How do we accelerate materials development and deployment
- Vídeo: Emergence of multi-stakeholder initiatives
- Vídeo: The Materials Innovation Ecosystem
- Vídeo: Part 1:Multiscale Modeling and Multilevel Design of Materials
- Vídeo: Part 2: Multiscale Modeling and Multilevel design of Materials
- Vídeo: Decision-Making in Material Design
- Vídeo: Multilevel Systems-Based Materials Design
- Discussion Prompt: Assignment #1
- Discussion Prompt: Assignment #2
- Reading: Earn a Georgia Tech Badge/Certificate/CEUs
- Vídeo: Material Property, Material Structure, and Manufacturing Processes
- Vídeo: Process-Structure-Property (PSP) Linkages
- Vídeo: Role of Structures in PSP Linkages
- Vídeo: Data Science Terminology
- Vídeo: Main Components of Data Science
- Vídeo: What is Big Data?
- Vídeo: Digital Representation of Material Structure
- Vídeo: Spatial Correlations: n-Point Statistics
- Vídeo: Computation and Visualization of 2-Point Spatial Correlations
- Vídeo: Principal Component Analyses (PCA) for low dimensional representations
- Vídeo: Principal Component Analyses (PCA) for low dimensional representation of material structure
- Vídeo: Homogenization: Passing Information to Higher Length Scales
- Vídeo: Structure-Property Linkages using a Data Science Approach-Part 1
- Vídeo: Structure-Property Linkages using a Data Science Approach-Part 2
- Vídeo: Materials Innovation Ecosystem
- Vídeo: Materials Innovation Cyberinfrastucture
- Vídeo: e-Collaboration Platforms/Environments
- Vídeo: Materials Cyber-Infrastructure
- Vídeo: Introduction to PyMKS Materials Knowledge Systems in Python
- Vídeo: Materials Data Science with PyMKS
- Reading: PyMKS website
- Discussion Prompt: PyMKS output
- Discussion Prompt: Auto and Crosscorrelation for a microstructure
- Reading: Take another course like this !
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Excellent course if you are looking to understand how to design high performance materials leveraging current advances in data sciences.
Very well delivered by Dr. Surya Kalidindi and Prof McDowell. Reference to the book on the subject by Dr. Kalidindi supplemented by web search was useful.
Need to put the new skills acquired, in practice at work, where I see a huge potential.
Thanks Georgia Tech!!
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!
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 valuable course for materials modelling enthusiast. It provides me the firm grounding and preparation for my future research work in this material modeling. This course is a fine balance of technical knowledge, its implementation and the practical approaches one needs to adopt to effectively use this knowledge of materials modeling in real world. (Anupam Purwar)