cof-landscaper 2026.5.12.4

Python toolkit for building and analyzing 2D COF stacking-energy landscapes.

COF-Landscaper

COF-Landscaper is a Python package for building and analyzing 2D COF stacking-energy landscapes.

Researchers interested in applying COF-Landscaper to their own systems are welcome to contact me at [e-mail], particularly if they are unable or prefer not to install and run the workflow themselves. Depending on availability and the scope of the project, I may be able to provide support or explore a possible collaboration.

Platform Support

• Tested on macOS and Linux.
• Microsoft Windows is currently not tested.

Install From Source (PyPI release planned)

Create a virtual environment with Python 3.12.

python3.12 -m venv test-coflandscaper

Activate the environment.

source test-coflandscaper/bin/activate

Confirm the active Python executable.

which python

Confirm the Python version is 3.12.

python --version

Upgrade pip.

pip install --upgrade pip

Confirm pip is available.

pip --version

Clone the repository.

git clone https://github.com/GregorLauter/COF-Landscaper.git

Enter the project directory.

cd COF-Landscaper

Install the package.

pip install .

Install the Jupyter kernel package.

pip install ipykernel

Register this environment as a Jupyter kernel.

python -m ipykernel install --user --name test-coflandscaper --display-name "Python (test-coflandscaper)"

Workflow Notes

• The DFT workflow requires additional external HPC infrastructure.
• The MLIP workflow can be executed fully on a local machine.
• Workflow diagram:

Example Notebook

• Example notebook location in this repository: examples/COF-1/0_all/cof-landscaper.ipynb
• After installation, you can work from any project folder on your computer.
• A practical workflow is to copy the example notebook into your own project directory and keep the original examples folder as a reference.

Required Input Files

• The workflow requires separate node and linker fragments provided as .xyz files.
• Input fragments should ideally be pre-optimized with a generic force field, such as UFF, to remove severe steric clashes and obtain reasonable approximate bond lengths.
• The subsequent pre-optimization step handles the assembled framework. Therefore, the main requirement at this stage is that the individual fragments are chemically sensible and can be connected cleanly by the builder.
• The .xyz files can be prepared using any suitable molecular editor or visualizer, for example Avogadro, Mercury, or ChemDraw.

VS Code Recommendation

VS Code is (personally) recommended for running and editing the notebook and Python code.

To use the correct kernel in VS Code:

1. Open the notebook.
2. Click the kernel selector in the top-right.
3. Choose Python (test-coflandscaper).
4. Run a test cell such as import coflandscaper as cl.

Where To Find Explanations

• A stepwise explanation of the computational workflow is provided in the Markdown cells of the example notebook.
• Methodological details, assumptions, and validation context are documented in the accompanying manuscript [insert link here].