Metadata-Version: 2.1
Name: sgdml
Version: 0.2.0.dev1
Summary: Reference implementation of the GDML and sGDML force field models.
Home-page: http://www.gdml.ml
Author: Stefan Chmiela
Author-email: noreply@chmiela.com
License: MIT
Description: # Symmetric Gradient Domain Machine Learning (sGDML)
        
        For more details visit: [http://quantum-machine.org/gdml/doc/](http://quantum-machine.org/gdml/doc/)
        
        #### Requirements:
        - Python 2.7
        - NumPy (>=1.13.0)
        - SciPy
        
        ## Getting started
        
        ### Stable release
        
        Most systems come with the default package manager for Python ``pip`` already preinstalled. We install ``sgdml`` by simply calling:
        
        `pip install sgdml`
        
        The ``sgdml`` command-line interface and the corresponding Python API can now be used from anywhere on the system.
        
        ### Development version
        
        #### Clone the repository
        
        `git clone https://github.com/stefanch/sGDML.git`
        
        `cd sGDML`
        
        ...or update your existing local copy with
        
        `git pull origin master`
        
        ##### Install
        
        `pip install -e .`
        
        Using 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.
        
        ## Reconstruct your first force field
        
        Download one of the example datasets:
        
        `sgdml-get dataset ethanol`
        
        Train a force field model:
        
        `sgdml all ethanol.npz 200 1000 5000`
        
        ## Query a force field
        
        ```python
        import numpy as np
        from sgdml.predict import GDMLPredict
        from sgdml.utils import io
        
        r,_ = io.read_xyz('examples/geometries/ethanol.xyz') # 9 atoms
        print r.shape # (1,27)
        
        model = np.load('models/ethanol.npz')
        gdml = GDMLPredict(model)
        e,f = gdml.predict(r)
        print e.shape # (1,)
        print f.shape # (1,27)
        ```
        
        ## References
        
        * [1] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R.,
        *Machine Learning of Accurate Energy-conserving Molecular Force Fields.*
        Science Advances, 3(5), e1603015 (2017)   
        [10.1126/sciadv.1603015](http://dx.doi.org/10.1126/sciadv.1603015)
        
        * [2] Chmiela, S., Sauceda, H. E., Müller, K.-R., & Tkatchenko, A.,
        *Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields.*
        Nature Communications, 9(1), 3887 (2018)   
        [10.1038/s41467-018-06169-2](https://doi.org/10.1038/s41467-018-06169-2)
        
        * [3] Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., & Tkatchenko, A.,
        *sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning.*
        [arXiv:1812.04986](https://arxiv.org/abs/1812.04986)
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2 :: Only
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
