hiPhive

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 extraction can be found in the hiphive-examples repository <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 which it can be used in various ways, e.g., for generating phonon dispersions, computing phonon lifetimes, or running molecular dynamics simulations.

For questions and help please use the hiphive discussion forum on matsci.org <https://matsci.org/hiphive>. hiPhive and its development are hosted on gitlab <https://gitlab.com/materials-modeling/hiphive>.

Installation

hiPhive can be installed via pip::

pip3 install hiphive

or via conda <https://anaconda.org/conda-forge/hiphive>_::

conda install -c conda-forge hiphive

hiPhive requires Python3 and invokes functionality from several external libraries including the atomic simulation environment <https://wiki.fysik.dtu.dk/ase>, scikit-learn <http://scikit-learn.org/>, spglib <https://phonopy.github.io/spglib/>, SymPy <http://www.sympy.org/en/index.html>, and trainstation <https://trainstation.materialsmodeling.org/>. Please consult the installation section of the user guide <https://hiphive.materialsmodeling.org/installation.html> for details.

Credits

hiPhive has been developed at the Department of Physics <https://www.chalmers.se/en/departments/physics/Pages/default.aspx>_ of Chalmers University of Technology <https://www.chalmers.se/>_ (Gothenburg, Sweden) in the Condensed Matter and Materials Theory division <http://www.materialsmodeling.org>_.

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>_

Please consult the Credits <https://hiphive.materialsmodeling.org/credits>_ page of the documentation for additional references.