Metadata-Version: 2.1
Name: sgdml
Version: 0.4.10
Summary: Reference implementation of the GDML and sGDML force field models.
Home-page: http://www.sgdml.org
Author: Stefan Chmiela
Author-email: sgdml@chmiela.com
License: LICENSE.txt
Description: # Symmetric Gradient Domain Machine Learning (sGDML)
        
        For more details visit: [http://sgdml.org/](http://sgdml.org/)
        
        Documentation can be found here: [http://sgdml.org/doc/](http://sgdml.org/doc/)
        
        #### Requirements:
        - Python 3.7+
        - NumPy (>=1.19)
        - SciPy (>=1.1)
        
        #### Optional:
        - PyTorch (for GPU acceleration)
        - ASE (>=3.16.2) (to run atomistic simulations)
        
        ## Getting started
        
        ### Stable release
        
        Most systems come with the default package manager for Python ``pip`` already preinstalled. 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
        
        #### (1) Clone the repository
        
        ```
        $ git clone https://github.com/stefanch/sGDML.git
        $ cd sGDML
        ```
        
        ...or update your existing local copy with
        
        ```
        $ git pull origin master
        ```
        
        #### (2) Install
        
        ```
        $ pip install -e .
        ```
        
        Using the flag ``--user``, you 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.
        
        
        ### Optional dependencies
        
        Some functionality of this package relies on third-party libraries that are not installed by default. These optional dependencies (or "package extras") are specified during installation using the "square bracket syntax":
        
        ```
        $ pip install sgdml[<optional1>,<optional2>]
        ```
        
        #### GPU acceleration (via PyTorch)
        
        To enable GPU support, you need to install the optional [PyTorch](https://pytorch.org/) dependency using the ``torch`` keyword:
        
        ```
        $ pip install sgdml[torch]
        ```
        
        
        #### Atomic Simulation Environment (ASE)
        
        If you are interested in interfacing with [ASE](https://wiki.fysik.dtu.dk/ase/) to perform atomistic simulations (see [here](http://quantum-machine.org/gdml/doc/applications.html) for examples), use the ``ase`` keyword:
        
        ```
        $ pip install sgdml[ase]
        ```
        
        ## Reconstruct your first force field
        
        Download one of the example datasets:
        
        ```
        $ sgdml-get dataset ethanol_dft
        ```
        
        Train a force field model:
        
        ```
        $ sgdml all ethanol_dft.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('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)
        ```
        
        ## Authors
        
        * Stefan Chmiela
        * Jan Hermann
        
        We appreciate and welcome contributions and would like to thank the following people for participating in this project:
        
        * Huziel Sauceda
        * Igor Poltavsky
        * Luis Gálvez
        * Danny Panknin
        * Grégory Fonseca
        
        ## 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.*
        Computer Physics Communications, 240, 38-45 (2019)
        [10.1016/j.cpc.2019.02.007](https://doi.org/10.1016/j.cpc.2019.02.007)
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 :: 3.7
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: torch
Provides-Extra: ase
