# <img src=https://raw.githubusercontent.com/aiqm/torchani/master/logo1.png width=180/>  Accurate Neural Network Potential on PyTorch

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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.

<img src=https://raw.githubusercontent.com/aiqm/torchani/master/logo2.png width=500/>

# Install

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

```bash
pip install numpy
pip install torch -f https://download.pytorch.org/whl/nightly/cu100/torch.html
```

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

```bash
pip install --upgrade torch -f https://download.pytorch.org/whl/nightly/cu100/torch.html
```

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

```bash
pip install torchani
```

See also [PyTorch's official site](https://pytorch.org/get-started/locally/) for instructions of installing latest preview version of PyTorch.

Please install nightly PyTorch through `pip install` instead of `conda install`. If your PyTorch is installed through `conda install`, then `pip` would mistakenly recognize the package name as `torch` instead of `torch-nightly`, which would cause dependency issue when installing TorchANI.

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

```bash
./download.sh
```

# 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](https://github.com/isayev/ASE_ANI)

# Develop

To install TorchANI from GitHub:

```bash
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:
```bash
pip install sphinx sphinx-gallery pillow matplotlib sphinx_rtd_theme
```

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

# 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.