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        "author": "Stefan Chmiela",
        "author_email": "sgdml@chmiela.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "# Symmetric Gradient Domain Machine Learning (sGDML)\n\nFor more details visit: [http://sgdml.org/](http://sgdml.org/)\n\nDocumentation can be found here: [http://sgdml.org/doc/](http://sgdml.org/doc/)\n\n#### Requirements:\n- Python 2.7/3.7\n- NumPy (>=1.13.0)\n- SciPy\n- PyTorch (optional)\n\n## Getting started\n\n### Stable release\n\nMost systems come with the default package manager for Python ``pip`` already preinstalled. Install ``sgdml`` by simply calling:\n\n`pip install sgdml`\n\nThe ``sgdml`` command-line interface and the corresponding Python API can now be used from anywhere on the system.\n\n### Development version\n\n#### (1) Clone the repository\n\n`git clone https://github.com/stefanch/sGDML.git`\n\n`cd sGDML`\n\n...or update your existing local copy with\n\n`git pull origin master`\n\n#### (2) Install\n\n`pip install -e .`\n\nUsing the flag ``--user``, we can tell ``pip`` to install the package to the current users's home directory, instead of system-wide. This option might require you to update your system's ``PATH`` variable accordingly.\n\n##### ...with GPU support\n\nFor GPU support, the optional PyTorch dependency needs to be installed.\n\n`pip install -e .[torch]`\n\n## Reconstruct your first force field\n\nDownload one of the example datasets:\n\n`sgdml-get dataset ethanol_dft`\n\nTrain a force field model:\n\n`sgdml all ethanol_dft.npz 200 1000 5000`\n\n## Query a force field\n\n```python\nimport numpy as np\nfrom sgdml.predict import GDMLPredict\nfrom sgdml.utils import io\n\nr,_ = io.read_xyz('examples/geometries/ethanol.xyz') # 9 atoms\nprint r.shape # (1,27)\n\nmodel = np.load('models/ethanol.npz')\ngdml = GDMLPredict(model)\ne,f = gdml.predict(r)\nprint e.shape # (1,)\nprint f.shape # (1,27)\n```\n\n### ...with GPU support\n\nSetting ``use_torch=True`` when instantiating the predictor redirects all calculations to PyTorch.\n\n```python\ngdml = GDMLPredict(model, use_torch=True)\n```\n\n**_NOTE:_**  PyTorch must be installed with GPU support, otherwise the CPU is used. However, we recommend performing CPU calculations without PyTorch for optimal performance.\n\n\n## References\n\n* [1] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Sch\u00fctt, K. T., M\u00fcller, K.-R.,\n*Machine Learning of Accurate Energy-conserving Molecular Force Fields.*\nScience Advances, 3(5), e1603015 (2017)   \n[10.1126/sciadv.1603015](http://dx.doi.org/10.1126/sciadv.1603015)\n\n* [2] Chmiela, S., Sauceda, H. E., M\u00fcller, K.-R., & Tkatchenko, A.,\n*Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields.*\nNature Communications, 9(1), 3887 (2018)   \n[10.1038/s41467-018-06169-2](https://doi.org/10.1038/s41467-018-06169-2)\n\n* [3] Chmiela, S., Sauceda, H. E., Poltavsky, I., M\u00fcller, K.-R., & Tkatchenko, A.,\n*sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning.*\nComputer Physics Communications, 240, 38-45 (2019)\n[10.1016/j.cpc.2019.02.007](https://doi.org/10.1016/j.cpc.2019.02.007)\n\n* [4] Sauceda, H. E., Chmiela, S., Poltavsky, I., M\u00fcller, K.-R., & Tkatchenko, A.,\n*Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces.*\nThe Journal of Chemical Physics, 150, 114102 (2019)\n[10.1063/1.5078687](https://doi.org/10.1063/1.5078687)\n\n",
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        "summary": "Reference implementation of the GDML and sGDML force field models.",
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