Courses Detail Information

MSE6502J – Computational Approaches in MSE

Instructors:

Credits: 3

Pre-requisites: Fundamentals of materials science; Basic knowledge of computer programming.

Description:

This course provides a comprehensive introduction to the theories and applications of conventional modeling and simulation techniques in the field of materials science and engineering. Specifically, the course focuses on the basic mathematic and physical theories for density functional theory (DFT), molecular dynamics (MD) and phase field method (PFM), together with their implementations and practical operations. In addition, the course discusses the applications of the emerging artificial intelligence techniques and supercomputers, with hands-on exercise on the design of machine learning models and the realization of basic parallel computing algorithms.

Course Topics:

Course introduction and an overview of computational materials science
Relevant materials science background knowledge;
Basic Linux operations and algorithms I (hands-on)
A short intro to quantum mechanics
Density functional theory I;
Basic Linux operations and algorithms II (hands-on)
Density functional theory II
Applications of DFT and case studies
A short intro to statistical mechanics
Molecular dynamics
Phase field method I
Phase field method II;
Applications of PFM and case studies
Introduction to other common computational models
Tomb-sweeping Day
Multi-scale modeling approaches
Adjusted to April 10
Machine learning for materials science and related case studies