Computational Materials Science: DFT, MD, and ML
10-module course on computational materials — density functional theory, molecular dynamics, and ML for materials discovery.
Key Highlights
- ✓ 10 modules with computational labs
- ✓ DFT calculations with VASP and Quantum ESPRESSO
- ✓ Molecular dynamics with LAMMPS
- ✓ ML for materials property prediction
- ✓ High-throughput screening
- ✓ Capstone: discover a new material
Overview
A 10-module course on computational materials science. Covers density functional theory (DFT), molecular dynamics (MD), and machine learning for materials discovery.
What's Inside
Course Content
Modules: (1) Crystal Structures, (2) DFT Fundamentals, (3) DFT Practice (VASP/QE), (4) Molecular Dynamics, (5) MD Practice (LAMMPS), (6) ML for Materials, (7) Materials Databases, (8) High-Throughput Screening, (9) Property Prediction, (10) Capstone.
Computational Resources
Students get access to HPC clusters for running DFT and MD simulations. The capstone involves screening 1000+ compounds for a target property using a combination of DFT and ML.
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