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# <img src=https://raw.githubusercontent.com/aiqm/torchani/master/logo1.png width=180/>  Accurate Neural Network Potential on PyTorch
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[![Codefresh build status]( https://g.codefresh.io/api/badges/pipeline/zasdfgbnm/aiqm%2Ftorchani%2Ftorchani?branch=master&type=cf-1)]( https://g.codefresh.io/repositories/aiqm/torchani/builds?filter=trigger:build;branch:master;service:5babc52a8a90dc40a407b05f~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.
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<img src=https://raw.githubusercontent.com/aiqm/torchani/master/logo2.png width=500/>

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# Install

TorchANI requires the master branch of PyTorch, which means:

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You need to install the latest preview version of pytorch\
For install
```bash
conda install pytorch-nightly -c pytorch
```
For update
```bash
conda update pytorch-nightly
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```
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After installing the correct PyTorch, all you need is clone the repository and do:

```bash
pip install .
```

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After TorchANI has been installed, you can build the documents by running `sphinx-build docs build`. But make sure you
install dependencies:
```bash
pip install sphinx sphinx-gallery pillow matplotlib sphinx_rtd_theme
```
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# 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
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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.

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Code review is required before merging pull request.

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