Calendar
| Week | Topic | Details | Practical | Assignment | Quiz |
|---|---|---|---|---|---|
| 1 | Orientation | Course arrangement Overview of the course Open-source software git, Github & Gitlab | Setup Python environment and use jupyter notebook Git, Github & Gitlab | ||
| 2 | CNY Week | ||||
| 3 | Computer and Computation | materials informatics, computer, programming language, performance, high-performance computing (HPC) open-sourced software | |||
| 4 | Database | data structure, database format, materials database, data generation, materials selection visualization data mining how to query database data visualization data mining | query data from materials project simple data visualization and data mining (bulk modulus) | ||
| 5 | Atomistic Structures I | molecules, graph, biochemistry, nanomaterials, crystalline materials, noncrystalline materials, ordering, prototypes, Wyckoff positions, structure map symmetry, group theory, point groups, space groups, primitive cells, conventional cells, Brillouin zone, reciprocal space & path | |||
| 6 | Atomistic Structures II | defect interface grain boundary | use pymatgen, ase, and spglib to build models and symmetry analysis use VESTA and ase-gui for structure visualization | 1 | 1 |
| 7 | Models and Theories I | multiscale modelling, atomistic modelling, force field, codes molecular dynamics and monte carlo | 2 | ||
| 8 | Models and Theories II | quantum mechanics, electronic structure, density functional theory (DFT) functionals, plane waves and pseudopotentials, codes | use ase to load force field and do some computation (equation of state, stability of diamond and graphite) use ase to run molecular dynamics | ||
| 9 | Optimization | introduction, energy landscapes local optimization: 1D, 2D & beyond optimization, performance gradients, steepest descent, curvature global optimization: genetic algorithm, basin hopping, stimulus annealing no free lunch rule | 3 | ||
| 10 | High-throughput Simulation | thermodynamics and kinetics finite temperature and entropy convex hulls phase diagram AIRSS Genetic Algorithm | 2 | ||
| 11 | Machine Learning I | regression and fitting neural networks Decision tree & random Forest classification principal component analysis clustering | use pytorch learn to run some simple classification training, errors | 4 | |
| 12 | Machine Learning II | Graph neural network Diffusion model | Pytorch for GNNs | ||
| 13 | Machine Learning Potential | descriptor: SOAP, ACE, GAP and etc. errors MACE | MACE examples | 3 | 5 |