Aims and Fit of Module

The aim of this module is to provide students with a comprehensive understanding of classical molecular simulation and machine learning techniques and their applications in material science. Through theoretical concepts, practical simulations, and hands-on experience with software tools, students will gain the skills and knowledge required to model and analyze materials at the molecular level.

Learning outcomes

A understand the principles and differences between molecular dynamics and Monte Carlo simulations,
B: develop the ability to critically assess the reliability and validity of molecular simulation studies presented in the literature,
C: decide the appropriate molecular simulation approach based on the application to research, and design a molecular simulation strategy applicable to problems,
D: acquire a comprehensive understanding of the functionalities and constraints of standard molecular simulation software.

Method of teaching and learning

26 one-hour lectures, 26 one-hour computer lab practicals and a computational research project that could act as a pre-project for students’ thesis research project.