Metadata-Version: 1.2
Name: hiphive
Version: 0.7
Summary: High-order force constants for the masses
Home-page: http://hiphive.materialsmodeling.org/
Maintainer: The hiPhive developers team
Maintainer-email: hiphive@materialsmodeling.org
License: UNKNOWN
Description: hiPhive
        =======
        
        **hiPhive** is a tool for efficiently extracting high-order force constants
        from atomistic simulations, most commonly density functional theory
        calculations. A detailed description of the functionality provided as well as an
        extensive tutorial can be found in the
        `user guide <https://hiphive.materialsmodeling.org/>`_. Complete examples of
        using hiphive for force constants extaction can be found at `hiphive examples <https://gitlab.com/materials-modeling/hiphive-examples/>`_.
        
        **hiPhive** is written in Python, which allows
        easy integration with countless first-principles codes and analysis tools
        accessible in Python, and allows for a simple and intuitive user interface. For
        example using the following snippet one can train a force constant potential:
        
        .. code-block:: python
        
           cs = ClusterSpace(primitive_cell, cutoffs)
           sc = StructureContainer(cs, list_of_training_structure)
           opt = Optimizer(sc.get_fit_data())
           opt.train()
           fcp = ForceConstantPotential(cs, opt.parameters)
        
        after wich it can be used in various ways, e.g., for generating phonon
        dispersions, computing phonon lifetimes, or running molecular dynamics
        simulations.
        
        
        Installation
        ------------
        
        **hiPhive** can be installed via `pip`::
        
            pip3 install hiphive
        
        If you want to get the absolutely latest (development) version you can clone
        the repo and then install **hiPhive** via::
        
          git clone git@gitlab.com:materials-modeling/hiphive.git
          cd hiphive
          python3 setup.py install --user
        
        **hiPhive** requires Python3 and invokes functionality from
        several external libraries including the
        `atomic simulation environment <https://wiki.fysik.dtu.dk/ase>`_,
        `spglib <https://atztogo.github.io/spglib/>`_ and
        `SymPy <http://www.sympy.org/en/index.html>`_.
        Please note that the dependency on
        `scikit-learn <http://scikit-learn.org/>`_
        is not enforced during installation via `pip`.
        Please consult the
        `installation section of the user guide <https://hiphive.materialsmodeling.org/installation.html>`_
        for details.
        
        
        Credits
        -------
        
        * Fredrik Eriksson
        * Erik Fransson
        * Paul Erhart
        
        **hiPhive** has been developed at Chalmers University of Technology in
        Gothenburg, Sweden, in the
        `Materials and Surface Theory division <http://www.materialsmodeling.org>`_
        at the Department of Physics.
        
        When using **hiphive** in your research please cite the following paper:
        
        | Fredrik Eriksson, Erik Fransson, and Paul Erhart
        | *The Hiphive Package for the Extraction of High‐Order Force Constants by Machine Learning*
        | Adv. Theory. Sim., 1800184 (2019)
        | `doi: 10.1002/adts.201800184 <https://doi.org/10.1002/adts.201800184>`_
        
        Also consult the `Credits <https://hiphive.materialsmodeling.org/credits>`_
        page of the documentation for additional references.
        
        **hiphive** and its development are hosted on
        `gitlab <https://gitlab.com/materials-modeling/hiphive>`_.
        Bugs and feature requests are ideally submitted via the
        `gitlab issue tracker <https://gitlab.com/materials-modeling/hiphive/issues>`_.
        The development team can also be reached by email via
        hiphive@materialsmodeling.org.
        
Platform: unix
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Physics
