Installation¶
Recommended Environment¶
It is recommended to install AIMMD in a clean conda environment. Python 3.13 is tested and supported, but other versions may work as well. Create a new environment with:
conda create -n aimmd python=3.13
Prerequisites¶
AIMMD depends on a working GROMACS installation. The package expects either
gmx or gmx_mpi to be available on PATH because import-time
initialization resolves the executable and uses it to configure engine-facing
defaults.
For lightweight testing, the GROMACS can be installed from
conda-forge:
conda install conda-forge::gromacs
For production work, we recommend building GROMACS from source for better performance and cluster-specific tuning.
Installing AIMMD¶
Install the latest release from PyPI:
pip install aimmd-lab
The distribution is named aimmd-lab, but the import name is aimmd:
import aimmd
To work on AIMMD itself, install from a local clone in editable mode with the optional test and documentation dependencies:
git clone https://github.com/covinolab/AIMMD.git
cd AIMMD
pip install -e ".[tests,docs]"
Optional Graph-Network Dependencies¶
Graph-neural-network workflows need extra packages that are not installed by
default (torch-cluster in particular can be awkward to build). The graphs
extra pulls in torch-geometric and torch-cluster:
pip install "aimmd-lab[graphs]"
pip install mlcolvar
If the default wheels do not match your CUDA / Python build, install them explicitly. The following is one confirmed-working example for Linux with an NVIDIA GPU on CUDA 11.8 and Python 3.13 — adjust the versions and index URLs for your own setup:
pip install torch==2.7.1 -f https://download.pytorch.org/whl/cu118/torch-2.7.1%2Bcu118-cp313-cp313-manylinux_2_28_x86_64.whl
pip install torch-geometric==2.7.0
pip install torch-cluster==1.6.3 -f https://data.pyg.org/whl/torch-2.7.0%2Bcu118/torch_cluster-1.6.3%2Bpt27cu118-cp313-cp313-linux_x86_64.whl
pip install mlcolvar
These packages matter only if you use the optional graph utilities in
aimmd.network.graph_utils or graph-based descriptor pipelines.
Verifying the Installation¶
The installation can be verified by running the test suite.
pip install pytest
pytest tests/
Building the Documentation¶
The Sphinx sources live under docs/source. Install the documentation
dependencies and build from the docs directory:
pip install -r docs/requirements.txt
make html
The generated HTML is written to docs/build/html. See the
Developer Guide for how the build imports the package without the heavy
runtime dependencies.