Link to Canvas
Links to lecture materials are below. Each lecture covers a big topic, and generally it takes several academic lectures to cover each of them.
Useful books related to the course are placed here
Lecture 1 – Basic introduction to quantum physics: reminder about de Broglie wave function, Max Born hypothesis, Heisenberg uncertainty principle, Schrödinger equation
Lecture 2 – Introduction to materials properties: Structure of materials, Drude model of metals, Hall effect, Thermal conductivity, Fermi-Dirac distribution, Sommerfeld theory
Lecture 3 – Quantum chemical methods: Density of electronic states, Bloch’s theorem, Hartree approximation, Hartree-Fock method, DFT, Kohn-Sham equations, Brillouin zone, Pseudopotentials
Lecture 3a – Spin-orbit coupling in DFT
Lecture 4 – Classical molecular dynamics: Born-Oppenheimer approximation, Molecular mechanics, Interatomic potentials, Force fields, ML potentials, Molecular dynamics, Monte-Carlo
Lecture 5 – Computational prediction of materials: How to predict materials, Crystal structure and properties, Idea of global optimization, Crystal structure prediction methods, Local optimization, USPEX
Lecture 6 – Mechanical properties of materials: Small deformation of solid state, Mechanical stresses, Hook’s law, Elastic constants, Elastic moduli, Stress-strain diagram, AI for hardness
Lecture 7 – AI and ML in materials science: General concepts of AI, Algorithms of AI, Examples where and how AI applied to solve general issues of computational materials science
Lecture 8 - Machine learning interatomic potentials: classical potentials vs. machine learning
Tutorials on Quantum ESPRESSO is available on GitHub page. Be aware! to complete metaGGA calculations one need to compile QE with libxc library, which is not a trivial issue, but works well for version 6.7