Accurate Neural Network Potential on PyTorch

Metrics:

Checks:

Deploy:

We only provide compatibility with nightly PyTorch, but you can check if stable PyTorch happens to be supported by looking at the following badge:

TorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull request.

Install

TorchANI requires the latest preview version of PyTorch. You can install PyTorch by the following commands (assuming cuda10):

pip install numpy
pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html

If you updated TorchANI, you may also need to update PyTorch:

pip install --upgrade --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html

After installing the correct PyTorch, you can install TorchANI by:

pip install torchani

See also PyTorch's official site for instructions of installing latest preview version of PyTorch.

To run the tests and examples, you must manually download a data package

./download.sh

Citation

Please cite the following paper if you use TorchANI

• Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, and Adrian E. Roitberg. TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials. Journal of Chemical Information and Modeling Just Accepted Manuscript,

• Please refer to isayev/ASE_ANI for ANI model references.

ANI model parameters

All the ANI model parameters including (ANI2x, ANI1x, and ANI1ccx) are accessible from the following repositories:

• isayev/ASE_ANI
• aiqm/ani-model-zoo

Develop

To install TorchANI from GitHub:

git clone https://github.com/aiqm/torchani.git
cd torchani
pip install -e .

After TorchANI has been installed, you can build the documents by running sphinx-build docs build. But make sure you install dependencies:

pip install sphinx sphinx-gallery pillow matplotlib sphinx_rtd_theme

To manually run unit tests, do python setup.py nosetests

If you opened a pull request, you could see your generated documents at https://aiqm.github.io/torchani-test-docs/ after you docs check succeed. Keep in mind that this repository is only for the purpose of convenience of development, and only keeps the latest push. The CI runing for other pull requests might overwrite this repository. You could rerun the docs check to overwrite this repo to your build.

Note to TorchANI developers

Never commit to the master branch directly. If you need to change something, create a new branch, submit a PR on GitHub.

You must pass all the tests on GitHub before your PR can be merged.

Code review is required before merging pull request.