MSE 726 Materials Informatics

3 Credit Hours

The course aims to introduce the emergent field of materials informatics and current approaches that employ informatics and experimental and computational data to accelerate the process of materials optimization, discovery and development. An emphasis will be placed on practical implementation of machine learning techniques to various materials science problems.

Prerequisite

Introductory Materials Science course (NC State MSE 500)

Course Objectives

The course aims to introduce the emergent field of materials informatics and current approaches that employ machine learning and experimental and computational data to accelerate the process of materials optimization, discovery and development. An emphasis will be placed on practical implementation of machine learning techniques to various materials science problems.

Course Outcomes

After completing this course, students will be able to:·Demonstrate an understanding of key materials informatics concepts and components; ·Explain the relationship between materials and data-driven techniques ·Interpret the informatics problems and capabilities associated with different types of materials ·Describe available machine learning techniques and materials databases ·Identify a machine learning algorithm with the desired properties for a given materials problem ·Evaluate existing and emerging machine learning technologies and analyze trends in data-driven techniques to anticipate how materials informatics evolve to meet changing needs

Course Requirements

Reading assignments: Reading assignments in the form of research papers, reviews and book chapters will be assigned throughout the semester. Each assignment will include a series of questions to be turned in and graded. The readings will be distributed via the course website.

Homework: Three hands-on homeworks will be assigned throughout the semester. Each homework will have a critical literature review and a hands-on experience.

Project: Hands-on project will be required in lieu of a final exam. Students will be able to carry out a project of their own design after approval by the instructor. The project involves application of machine learning to data collected throughout the materials research. The grading will be based on write-up, presentation and class participation.

Grading:

Homework 40% Reading Assignments 20% Project 40%

Course grades will be assigned following the usual 10 points per letter grade.