Courses

Application of Data Sciences to Materials Design, Development, and Deployment

This course will introduce fundamental principles of material science and engineering, as viewed in the larger technical context of data science. In particular, the Process-Structure-Property (PSP) linkage that unifies materials science, engineering design, and advanced manufacturing methods will form the intellectual basis for integrating data science techniques into a more coherent understanding of materials science and engineering. Relevant techniques will be presented so that students can gain familiarity with data science methods. Students will also gain experience with the MATIN e-collaboration platform for subsequent study within the broader community of materials scientists.

  1. course
  2. IDEASMD3
  3. materials data analytics
  4. materials data science
  5. materials informatics
  6. materials innovation
  7. Sandia National Labs

Division 1000 Data Sciences Education Program

 

Course: Application of Data Sciences to Materials Design, Development, and Deployment
Instructor: Surya Kalidindi, Professor, Mechanics of Materials, Georgia Tech
Duration: Two Days
Date: May 22nd and 23rd, 2017
Time: 8:30 am to 4:30 pm
Location: 858EL / L1421 (room may be revised depending on enrollment)

 

Course Description

This course will introduce fundamental principles of material science and engineering, as viewed in the larger technical context of data science. In particular, the Process-Structure-Property (PSP) linkage that unifies materials science, engineering design, and advanced manufacturing methods will form the intellectual basis for integrating data science techniques into a more coherent understanding of materials science and engineering. Relevant techniques will be presented so that students can gain familiarity with data science methods. Students will also gain experience with the MATIN e-collaboration platform for subsequent study within the broader community of materials scientists.

Prerequisites

• Complete Weeks 2 and 3 of the free course at this URL:
        https://www.coursera.org/learn/material-informatics
• General fluency in the principles of mechanics of materials

Recommended Skills

• Desire to learn more about data science
• Ability to participate in multidisciplinary discussions following each lecture

Course Format

This two-day course will consist of a series of short (45-50 minute) lectures on technical material, followed by group discussions (10-15 minutes) to connect the course material to topics of interest to Sandia staff and managers.

Course Objectives

At the completion of this course, participants should be able to:
• Understand basic principles of Process-Structure-Property linkages in material science
• Appreciate the role of data science methods in unifying various viewpoints in material science and engineering.

Text

No text required: appropriate course notes will be distributed for each lecture.

Expanded Course Outline

Day 1

Topic 1: Challenges and Opportunities in the Nexus Between Materials Science and Engineering and Design/Manufacturing.
Sandia Presentation 1: Highlight a practical example from ongoing projects at Sandia.
Course Notes

Topic 2: Mathematical Theory of Microstructure Representation. Introduce the concept of microstructure function and demonstrate its potential universality in addressing all material systems at all hierarchical length/structure scales.
Sandia Presentation 2: Highlight diversity of potential materials datasets at Sandia: the idea is to emphasize the complexity and richness of materials datasets.
Course Notes

Topic 3: Spatial correlations. Introduce n-point spatial correlations as a rigorous statistical measure of the material structure. Show why this formalism is the most systematic and rigorous approach available today. Discuss inverse mapping from statistics to microstructure realizations. Demonstrate codes available for easy computation of 2-point spatial correlations.
Sandia Presentation 3: Potential application case study for materials data sciences.
Course Notes

Topic 4: Low-rank measures of material structure. Discuss how PCA is an excellent tool for low-rank description of n-point statistics. Discuss the physical interpretation of PC scores and eigen PC maps. Interactive session continues from the previous session including PC.
Sandia Presentation 4: Potential application case study for materials data sciences.
Course Notes

Topic 5: Mathematical forms for establishing PSP linkages based on available composite theories. Brief overview of the existing theory for microstructure-sensitive homogenization and localization. Description of the PSP model forms consistent with the theory. Discuss of the limitations of the series form of these expressions. Discuss of convergence conditions.
Sandia Presentation 5: Potential application case study for materials data sciences.
Course Notes

Topic 6: Mathematical forms for establishing PSP linkages using statistical learning approaches based on Bayesian regression and Kriging models. Discussion of relative merits compared to approaches in Topic 5.
Sandia Presentation 6: Potential application case study for materials data sciences.
Course Notes


Day 2

Topic 7: Example Case Study in formulating a homogenization structure-property linkage.
Sandia Presentation 7: Potential application case study for materials data sciences.
Course Notes

Topic 8: Example Case Study in formulating a homogenization process-structure linkage.
Sandia Presentation 8: Potential application case study for materials data sciences.
Course Notes

Topic 9: Example Case Study in formulating a localization structure-property linkage.
Sandia Presentation 9: Potential application case study for materials data sciences.
Course Notes

Topic 10: Demonstration of MATIN as an e-collaboration platform
Sandia Presentation: Open-ended consulting with individuals or small teams on their datasets.
Course Notes