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Simulation_tutorials
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# Simulation Tutorials This repository contains Python-based hands-on materials developed for the Computational Materials and Data Science course at Seoul National University. The materials are based on the Fall 2024 course taught by Seokhyun Choung. The tutorials introduce a practical computational materials science workflow covering: - Python fundamentals - Density functional theory (DFT) with ASE and GPAW - Molecular dynamics (MD) - Regression and feature engineering - Machine-learning potentials (MLPs) with MACE and the Open Catalyst Project - CatBench and basic benchmarking workflows ## Repository Layout ```text . ├── notebooks/ # Main notebooks in recommended learning order ├── Class_materials/ # PDF lecture materials ├── data/ # Datasets used in the exercises ├── src/simulation_tutorials/ # Reusable utilities for the notebooks ├── ads_site_search.py # Compatibility wrapper for the legacy import path └── README.md ``` ## Notebook Roadmap | Order | Notebook | Topic | | --- | --- | --- | | 00 | `notebooks/00_intro_to_python.ipynb` | Python fundamentals and an introduction to the working environment | | 01 | `notebooks/01_dft_setup_and_bulk.ipynb` | GPAW installation, basic ASE structures, and bulk calculations | | 02 | `notebooks/02_dft_surface.ipynb` | Surface slab construction and visualization | | 03 | `notebooks/03_dft_adsorption.ipynb` | Adsorption structure generation and adsorption workflows | | 04 | `notebooks/04_dft_adsorption_applications.ipynb` | Extended adsorption examples and applications | | 05 | `notebooks/05_md_setup_and_simulation.ipynb` | MD setup, trajectory output, and GIF visualization | | 06 | `notebooks/06_md_displacement_analysis.ipynb` | MD trajectory interpretation and displacement analysis | | 07 | `notebooks/07_ml_regression_and_feature_engineering.ipynb` | Regression models, descriptors, and feature engineering | | 08 | `notebooks/08_mlp_intro_and_setup.ipynb` | MLP installation and basic usage | | 09 | `notebooks/09_mlp_adsorption_workflows.ipynb` | MLP-based surface and adsorption calculations | | 10 | `notebooks/10_mlp_benchmarking.ipynb` | Catalysis-Hub-related benchmarking workflows | | 11 | `notebooks/11_catbench.ipynb` | CatBench examples and applications | ## Appendix Notebooks | Notebook | Topic | | --- | --- | | `notebooks/appendix_advanced_dft_optimization.ipynb` | Advanced DFT optimization algorithms | | `notebooks/appendix_mace_calculator_setup.ipynb` | Loading and using a MACE calculator | | `notebooks/appendix_open_catalyst_calculator_setup.ipynb` | Setting up an Open Catalyst Project calculator | ## Utilities Reusable helper functions are organized under `src/simulation_tutorials/`: - `visualization.py`: structure rendering, repeated-slab visualization, and trajectory GIF generation - `optimization.py`: structure relaxation and energy-calculation helpers - `surface_sites.py`: surface-site search helpers - `progress.py`: a lightweight text progress bar The notebooks retain thin wrappers where needed to preserve the original calling conventions. ## Class Materials The `Class_materials/` directory contains the PDF materials used in class. Reading them alongside the notebooks provides additional context for the hands-on exercises. ## Data - `data/CO2RR_data.csv`: dataset used in the regression and feature-engineering exercises ## Getting Started ### Local Jupyter Environment Clone the repository and start Jupyter from the repository root: ```bash git clone https://github.com/s-choung/Simulation_tutorials.git cd Simulation_tutorials jupyter lab ``` The notebooks are configured to locate the `src/` directory when launched from either the repository root or the `notebooks/` directory. ### Google Colab Use the **Open in Colab** badge at the top of each notebook to launch it in Google Colab. When a notebook requires utilities from `src/simulation_tutorials/`, its bootstrap cell clones the repository automatically. ## Notes - Many notebooks include `apt-get` or `pip install` cells intended for Google Colab. - Package requirements vary by topic and may include ASE, GPAW, pymatgen, MACE, and Open Catalyst Project tools. - This repository is designed for teaching and hands-on practice rather than as a production-ready Python package. ## Credits - **Course:** Computational Materials and Data Science, Seoul National University - **Term:** Fall 2024 - **Instructor:** Seokhyun Choung
