# TorchANI

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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 master branch of PyTorch, which means:

* The pytorch installed by `pip install` or `conda install` would not work.
* You need to compile install the latest pytorch, see [official instructions](https://github.com/pytorch/pytorch#from-source).
* Some update to TorchANI might require the user to recompile install the latest PyTorch. Before submitting a bug report, make sure you are running the latest PyTorch.

After installing the correct PyTorch, all you need is clone the repository and do:

```bash
pip install .
```

After TorchANI has been installed, you can build the documents by running `sphinx-build docs build`.

# 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.

# 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`