Computational Materials Science at the Atomic Scale

3 credits · Graduate

Introduction to computational materials science methods at the atomistic level, with an emphasis on quantum methods. A brief history of computational approaches is presented, with deep dives into the most impactful methods: density functional theory, tight-binding, empirical potentials, and machine learning-based property prediction. Computation of optical, electronic, phonon properties. Bulk materials, interfaces, nanostructures. Molecular dynamics. Prerequisites - undergraduate quantum mechanics. Experience writing code is preferred but not required.