3 credits
Spring 2025 Lecture Upper Division
This course provides an introduction at the graduate level to theory and methods for simulating the structure and properties of materials. The course will provide a broad initial overview of many atomistic modeling techniques, followed by a detailed study of density functional theory (DFT) over several weeks, then a few lectures on classical molecular dynamics (MD) and Monte Carlo (MC) simulations, and finishing with an introduction to materials informatics techniques which rely on learning from materials data. Students will obtain an essential understanding of quantum and classical theory and become acquainted with practical methods for running DFT, MD and MC simulations, as well as combining such simulations with simple machine learning and data science methods for materials design. Existing and new tools on nanoHUB, access to open-source simulation software, and writing simple Python code will be utilized for all necessary coursework and exercises. Permission of department required.
Learning Outcomes1Design, execute and analyze simple simulations using techniques appropriate for the problem at hand.
2Recognize the approximations and level of accuracy associated with each modeling technique.
3Establish a foundation for exploring materials modeling techniques in greater depth and applying them to their own research projects.
4Recognize the importance of machine learning and data science, and identify when to apply such techniques for materials datasets.
Prerequisites
Restrictions
made by Purdue students.