This GitHub repository contains Jupyter notebooks that illustrate various aspects of polymerization reactions, along with the mathematical and computational methods used to model and analyze them. These resources are suitable for both self-learning and classroom instruction.
You can learn to do cool things like the example below! 🚀
The notebooks cover the following topics:
- Population Balances
- Monte Carlo Methods
- Radical Polymerization
- Living Polymerization
- Polycondensation
- Copolymerization
- Devolatilization
To open the notebooks in book format, just click here.
To run the notebooks, you can use one of three methods:
-
Google Colab — You can open the notebooks directly from the source code repository by selecting File -> Open notebook -> GitHub -> URL", and entering the following repository name:
HugoMVale/PRE-Notebooks
. This method requires a Google account but is likely the easiest for beginners. -
Binder — Binder also allows you to open the notebooks directly from the repository. However, you will need to wait for the container to be built. While suitable for beginners, this method requires a bit more patience, and the build process can occasionally fail, requiring a relaunch.
-
Local environment — Alternatively, you can clone or download this repository to your local machine. This method requires a local Python (>=3.10) installation as well as the packages listed in
requirements.txt
. Assuming you're running Windows and havegit
installed, follow these steps:git clone https://github.com/HugoMVale/PRE-Notebooks.git cd PRE-Notebooks python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -r requirements.txt
Contributions and improvements to these notebooks are welcome. If you have any suggestions, corrections, or additional materials that could enhance the learning experience, please consider submitting a pull request.
This project is provided under the very permissive MIT License.
We hope these notebooks provide a valuable resource for your journey into the world of (de)polymerization reaction modeling. Happy learning!