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"description": "# 
Accurate Neural Network Potential on PyTorch\n\nMetrics:\n\n\n\n\nChecks:\n\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n[](https://www.codefactor.io/repository/github/aiqm/torchani/overview/master)\n[](https://lgtm.com/projects/g/aiqm/torchani/alerts/)\n\nDeploy:\n\n[](https://github.com/aiqm/torchani/actions)\n[](https://github.com/aiqm/torchani/actions)\n\nTorchANI 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.\n\n
\n\n# Install\n\nTorchANI requires the latest preview version of PyTorch. You can install PyTorch by the following commands (assuming cuda10):\n\n```bash\npip install numpy\npip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html\n```\n\nIf you updated TorchANI, you may also need to update PyTorch:\n\n```bash\npip install --upgrade --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu100/torch_nightly.html\n```\n\nAfter installing the correct PyTorch, you can install TorchANI by:\n\n```bash\npip install torchani\n```\n\nSee also [PyTorch's official site](https://pytorch.org/get-started/locally/) for instructions of installing latest preview version of PyTorch.\n\nPlease 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.\n\nTo run the tests and examples, you must manually download a data package\n\n```bash\n./download.sh\n```\n\n# Paper\n\nThe original ANI-1 paper is:\n\n* 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.\n\nWe are planning a seperate paper for TorchANI, it will be available when we are ready for beta release of TorchANI.\n\nSee also: [isayev/ASE_ANI](https://github.com/isayev/ASE_ANI)\n\n# Develop\n\nTo install TorchANI from GitHub:\n\n```bash\ngit clone https://github.com/aiqm/torchani.git\ncd torchani\npip install -e .\n```\n\nAfter TorchANI has been installed, you can build the documents by running `sphinx-build docs build`. But make sure you\ninstall dependencies:\n```bash\npip install sphinx sphinx-gallery pillow matplotlib sphinx_rtd_theme\n```\n\nTo manually run unit tests, do `python setup.py nosetests`\n\nIf you opened a pull request, you could see your generated documents at https://aiqm.github.io/torchani-test-docs/ after you `docs` check succeed.\nKeep in mind that this repository is only for the purpose of convenience of development, and only keeps the latest push.\nThe CI runing for other pull requests might overwrite this repository. You could rerun the `docs` check to overwrite this repo to your build.\n\n# Note to TorchANI developers\n\nNever commit to the master branch directly. If you need to change something, create a new branch, submit a PR on GitHub.\n\nYou must pass all the tests on GitHub before your PR can be merged.\n\nCode review is required before merging pull request.\n\n\n",
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