README.md

Accurate Neural Network Potential on PyTorch

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

conda install pytorch-nightly -c pytorch

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

conda update pytorch-nightly

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.

Paper

The original ANI-1 paper is:

• Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical science. 2017;8(4):3192-203.

We are planning a seperate paper for TorchANI, it will be available when we are ready for beta release of TorchANI.

See also: isayev/ASE_ANI

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

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.

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