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matador is an aggregator, manipulator and runner of first-principles calculations, written with a bent towards battery electrode materials.
The source can be found on GitHub <https://github.com/ml-evs/matador> and online documentation is hosted at ReadTheDocs <https://docs.matador.science>.
Example Jupyter notebooks and tutorials can be found online <https://docs.matador.science/en/latest/examples_index.html>_ or in the examples/ folder of the matador source code.
Written & maintained by Matthew Evans <https://ml-evs.science>_ (2016-).
.. image:: docs/src/img/hull.png :name: LiPZn :align: center
In the simplest case (e.g. you already have Python 3.7+ set up), pip install matador-db is sufficient to get up and running, preferably in a fresh virtual environment.
Upgrading to the latest version should be as simple as pip install -U matador-db.
For an editable development installation, clone the source code from this repository and run pip install -e . from the matador folder. Tests can be run on your local machine with python -m unittest discover -v -b or simply with py.test after test dependencies have been installed with pip install .[test]. In order to test CASTEP-running functionality, the tests will look for an MPI-enabled executable named castep on your $PATH.
Further instructions can be found in the Installation instructions <https://docs.matador.science/en/latest/install.html>_.
matador is primarily a Python library that can be used inside Python scripts/modules to create a custom workflow. There are, however, several command-line scripts bundled with matador itself. All of these scripts are listed under CLI Usage <https://docs.matador.science/en/latest/cli.html>_.
For basic command-line usage, please explore the help system for each command. Common workflows can be found inside examples/ and in the online docs <http://docs.matador.science/en/latest/examples_index.html>_.
Please consult the full Python API documentation <http://docs.matador.science/en/latest/modules.html>_ for programmatic usage.
The API has many features that can be explored in the examples and API documentation. As a summary, matador can be used for:
run3. Tested on several supercomputers. run3 is designed primarily for simple workflows and offers little in the way of tools for creating complex workflows; if this is your use case, then check out some of the other codes listed below.This functionality is achieved by interfacing with much of the standard scientific Python stack (NumPy <https://numpy.org>, SciPy <https://scipy.org>, matplotlib <https://matplotlib.org>), some more materials-specific packages (spglib <https://github.com/atztogo/spglib/>, SeeK-path <https://github.com/giovannipizzi/seekpath>, periodictable <https://github.com/pkienzle/periodictable>) and other general packages (pymongo <https://github.com/mongodb/mongo-python-driver>, python-ternary <https://github.com/marcharper/python-ternary>, numba <https://numba.org>_).
This package is by no means unique in its functionality or goals. Below is a list of similar packages and an overview of where they overlap with matador:
ASE <https://wiki.fysik.dtu.dk/ase/>_: manipulation of structures, parsing and exporting files, running jobs and local databases with ase-db. An interface is provided to ASE's Atoms object.pymatgen <https://pymatgen.org>: similar to ASE, with a focus on the VASP DFT code. An interface is provided to pymatgen's Structure object. Local databases can be constructed with the pymatgen-db <https://github.com/materialsproject/pymatgen-db> add-on and high-throughput workflows are achieved with Fireworks <https://github.com/materialsproject/fireworks>_.AiiDA <https://www.aiida.net>_: high-throughput job running, provenance tracking and database storage for many simulation codes.sumo <https://github.com/SMTG-UCL/sumo>_ publication quality plotting, primarily for VASP but also with support for other codes.If you think this list is outdated, incorrect or simply incomplete, then please raise an issue!
If you use matador in your work, we kindly ask that you cite
Matthew L. Evans, Andrew J. Morris, *matador: a Python library for analysing, curating and performing high-throughput density-functional theory calculations* Journal of Open Source Software, 5(54), 2563 (2020)
`10.21105/joss.02563 <https://doi.org/10.21105/joss.02563>`_Source code archives for all versions above 0.9 can be found at Zenodo DOI 10.5281/zenodo.3908573 <https://doi.org/10.5281/zenodo.3908573>_.
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