PyTorch implementation of ANI
Project description
Accurate Neural Network Potential on PyTorch
Build:
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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 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.
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
./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
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
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.
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