Calendar
| Week | Topic | Details | Practical | Assignment | Quiz |
|---|---|---|---|---|---|
| 1 | Orientation | Course arrangement, materials informatics intro | Setup environment, and jupyter notebook | ||
| 2 | Programming | Python syntax review, NumPy fundamentals, Matplotlib, Jupyter notebook | Python programming, Copilot | ||
| 3 | Computer and Computation | Computer hardware, software, high-performance computing (HPC), open-sourced software | Git & Github, time complexity measurement | ||
| 4 | Database | Data structure, database format, materials database, data generation, materials selection, visualization, data mining (how to query database, data visualization, data mining) | Pandas, Materials Project API | ||
| 5 | Structures | Site, molecule, symmetry, defect & interface representation | pymatgen, VESTA | ||
| 6 | CNY Week | ||||
| 7 | Mid-Term Review | Review of Weeks 1-5 (orientation, programming, computing, database, structures), Q&A, practice problems | 1 | 1 | |
| 8 | Models and Theories I | Multiscale modelling, atomistic modelling, force field, codes, molecular dynamics and Monte Carlo | ASE | ||
| 9 | Models and Theories II | Quantum mechanics, electronic structure, density functional theory (DFT), functionals, plane waves and pseudopotentials, codes | Molecular dynamics and Monte Carlo | ||
| 10 | Optimization | Introduction, energy landscapes, local optimization, global optimization, structure prediction, no free lunch rule | Scipy, ASE (optimization) | ||
| 11 | High-throughput Simulation | Thermodynamics and kinetics, finite temperature and entropy, convex hulls, phase diagram | Atomate2 | ||
| 12 | Machine Learning Potentials | ML Potentials overview, Descriptors (SOAP, ACE, GAP), error analysis, MACE | MACE | ||
| 13 | Pre-Exam Review | Review of Weeks 8-12, Q&A, exam preparation | 2 | 2 |
Assignment due week shown in the Assignment column.