GPUMA

GPUMA is a minimalist Python toolkit for facile and rapid high-throughput molecular geometry optimization using machine-learning interatomic potentials (MLIPs).

Three model backends are supported out of the box:

• Fairchem UMA (UMA/OMol25) — the default backend.
• ORB-v3 (orbital-materials/orb-models).
• SevenNet (SevenNet), including multi-modal checkpoints such as 7net-omni.

All three backends support optional DFT-D3(BJ) dispersion correction.

GPUMA is especially designed for batch optimizations of many structures (conformer ensembles, datasets) on GPU, ensuring efficient parallelization and maximum GPU utilization by leveraging the torch-sim library. It wraps model backends and torch-sim functionality to provide both a simple command-line interface (CLI) and a small but expressive Python API for single- and multi-structure optimizations.

If conformer sampling is desired, GPUMA can generate conformer ensembles on the fly from SMILES strings. The SMILES → 3D embedding runs on the GPU via nvMolKit when a CUDA device is configured, and on the CPU via the morfeus library otherwise; the backend is chosen from technical.device like everywhere else in GPUMA. Alternative input formats are described in the CLI section below.

Feedback and improvements are always welcome!

Installation

Option 1: Install from PyPI (recommended)

This installs gpuma together with all dependencies (including the Fairchem UMA, ORB-v3, and SevenNet backends, as well as the nvMolKit GPU conformer-embedding backend). At the moment, installation and tests have only been validated under Python 3.12; using other Python versions is currently considered experimental.

GPU support: By default, pip install may pull a CPU-only build of PyTorch (especially on Windows). To enable GPU acceleration, install PyTorch with CUDA before installing GPUMA. Visit pytorch.org/get-started to get the install command for your platform and CUDA version, e.g.:

pip install torch --index-url https://download.pytorch.org/whl/cu124

• Using a uv virtual environment

  # create and activate a fresh environment (Python 3.12)
  uv venv .venv --python 3.12
  
  # activate the environment
  
  # install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org)
  uv pip install torch --index-url https://download.pytorch.org/whl/cu124
  
  # install gpuma from PyPI inside the environment
  uv pip install gpuma
  

• Using a conda environment

  # create and activate a fresh environment with Python 3.12
  conda create -n gpuma-py312 python=3.12
  conda activate gpuma-py312
  
  # install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org)
  pip install torch --index-url https://download.pytorch.org/whl/cu124
  
  # install gpuma from PyPI inside the environment
  pip install gpuma
  

GPU conformer embedding: SMILES → 3D conversion can run on the GPU via nvMolKit, which is a core dependency and installs automatically with GPUMA (it pins rdkit==2026.3.1 to match the RDKit build it is linked against). The backend is selected by technical.device in the config, as everywhere else in GPUMA: a CUDA device uses nvMolKit, otherwise the CPU (morfeus) backend is used and results stay valid. The GPU path needs an NVIDIA GPU (compute capability 7.0+); on CPU-only machines the package still installs and the CPU backend is used.

Option 2: Install from source

# clone the repository
git clone https://github.com/niklashoelter/gpuma.git
cd gpuma

# install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org)
pip install torch --index-url https://download.pytorch.org/whl/cu124

# install using (uv) pip
uv pip install .
# or, without uv:
pip install .

Documentation

Full documentation is available at https://niklashoelter.github.io/gpuma/.

For local browsing of the Markdown sources, see in particular:

• docs/index.md – overview and getting started
• docs/install.md – installation details
• docs/cli.md – CLI options and input formats
• docs/config.md – configuration file schema and examples
• docs/reference.md – API and configuration reference

Using a configuration file is highly recommended for reproducibility and ease of use.

Also check the examples/ folder in the repository for sample config files and usage examples:

• examples/config.json – Fairchem UMA configuration
• examples/config_orb.json – ORB-v3 configuration (with D3 options)
• examples/config_sevennet.json – SevenNet configuration (multi-modal 7net-omni)
• examples/example_single_optimization.py – single-structure optimization from Python
• examples/example_ensemble_optimization.py – ensemble/multi-structure optimization from Python
• examples/example_dispersion.py – DFT-D3(BJ) correction with Fairchem and ORB (single + batch)

Required for UMA models:
To access the UMA models on Hugging Face, you must provide a token either via the HUGGINGFACE_TOKEN environment variable or via the config (direct token string or path to a file containing the token).

CLI Usage

The CLI is provided via the command gpuma. For best results, create a config file (JSON or YAML) and reference it in all CLI calls (see examples/config.json for a minimal example).

Examples: Batch optimization of multiple XYZ structures

Optimize all XYZ files in a directory (each file containing a single structure):

gpuma batch --xyz-dir examples/example_input_xyzs/multi_xyz_dir/ --output output.xyz --config examples/config.json

Optimize multiple structures contained in a single multi-XYZ file:

gpuma batch --multi-xyz examples/example_input_xyzs/multi_xyz_file.xyz --output output.xyz --config examples/config.json

Refer to the CLI documentation for details on configuration options, supported input formats (SMILES, XYZ, directories, multi-XYZ files), and additional CLI examples.

Python API

A minimalistic and high-level Python API is provided for easy integration into custom scripts and workflows.

For example usage, see:

• examples/example_single_optimization.py
• examples/example_ensemble_optimization.py

Please refer to the documentation and examples for detailed usage examples and API reference.

Known limitations

When a run is started from SMILES, an RDKit/MMFF force field (via nvMolKit on GPU or the morfeus library on CPU) is used to generate an initial structure. Spin is not taken into account during this step, so the initial estimated geometries can be incorrect. When the MLIP models are applied subsequently, the structure can sometimes be optimized to a maximum rather than a minimum because the model is not provided with Hessian matrices. This behavior only affects runs originating from SMILES; it does not occur with better starting geometries (e.g., when starting from XYZ files).

Troubleshooting

• Fairchem/UMA: ensure network access for model downloads and optionally set or provide huggingface_token (e.g., via a token file) to access the UMA model family.
• ORB-v3: models are downloaded automatically on first use.
• SevenNet: models are downloaded automatically on first use; multi-modal checkpoints (e.g. 7net-omni, 7net-mf-ompa) also require a model_modal fidelity (see docs/config.md).
• D3 dispersion correction: set "d3_correction": true in the config to enable DFT-D3(BJ) for any backend (see docs/config.md).

License

MIT License (see LICENSE)