hiPhive 0.7.1

High-order force constants for the masses

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. Complete examples of using hiphive for force constants extaction can be found at 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:

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 [e-mail]: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, spglib and SymPy. Please note that the dependency on scikit-learn is not enforced during installation via pip. Please consult the installation section of the user guide 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 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

Also consult the Credits page of the documentation for additional references.

hiphive and its development are hosted on gitlab. Bugs and feature requests are ideally submitted via the gitlab issue tracker. The development team can also be reached by email via [e-mail].