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Installation

Requirements

  • Python 3.10 or newer
  • GROMACS available as gmx for build, sample, and validate
  • CP2K only if you want to run staged snapshot evaluation inputs
  • PLUMED only for PLUMED-biased systems

Create a small conda environment first:

mamba create -n bfflearn python=3.10 pip
mamba activate bfflearn

Install a matching PyTorch build for your machine before fitting or learning. Use the official PyTorch selector for the exact command:

https://pytorch.org/get-started/locally/

Example for Linux with CUDA 12.6:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

Then install BFF from PyPI:

pip install bfflearn

Notebook Support

The default installation keeps the command-line package small. To run the Jupyter examples, install the optional notebook tools:

pip install "bfflearn[notebook]"

This adds IPython, ipykernel, JupyterLab, Notebook, and widget support. You can then launch:

jupyter lab

If you need the exact code used for the published paper, do not install v0.0.1 through a direct pip Git URL. That archived tag predates the packaging cleanup. Instead, clone the repository, check out the archived tag, and follow the README.md and environment.yaml included in that snapshot:

git clone https://github.com/vojtechkostal/BayesicForceFields.git
cd BayesicForceFields
git checkout v0.0.1

That tag is the reference point for exact reproduction of the published paper data. The current bfflearn package is the post-paper refactored workflow.

PyTorch is intentionally not part of the default package dependencies because the correct CPU or CUDA build depends on the target hardware and driver stack.

Repository Environment

For work on the repository itself, create the shared project environment from the repository root:

mamba env create -f environment.yaml
mamba activate bfflearn

That environment installs BFF in editable mode together with the dev, docs, and notebook extras, but still leaves PyTorch to you so you can choose the correct CPU or CUDA build.

If you prefer to start from an existing environment:

pip install -e ".[dev,docs,notebook]"

Local Docs

Preview the docs locally:

mkdocs serve

Build the static site:

mkdocs build --strict