Metadata-Version: 2.4
Name: strongcoca
Version: 1.1
Summary: A package for simulating many-body interactions in nano and mesoscale systems.
Author: The strongcoca developer team
Maintainer: The strongcoca developer team
License-Expression: MPL-2.0
Project-URL: Documentation, https://strongcoca.materialsmodeling.org
Project-URL: Repository, https://gitlab.com/materials-modeling/strongcoca/
Keywords: chemistry,physics,scientific
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: ase
Requires-Dist: numpy>=1.14
Requires-Dist: scipy>=1.6
Provides-Extra: test
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Dynamic: license-file

strongcoca
**********

**strongcoca** is being developed as a sustainable, extensible, flexible and computationally efficient package for simulating coarse-grained many-body interactions in nano and mesoscale systems.
A detailed description of the functionality provided as well as tutorials can be found in the `user guide <https://strongcoca.materialsmodeling.org/>`_.

**strongcoca** can be installed via `pip`::

    pip install strongcoca

**strongcoca** has been developed at the Department of Physics of Chalmers University of Technology (Gothenburg, Sweden) and the Department of Chemistry at the University of Jyväskylä (Finland).

If you use **strongcoca** in your research please cite the following paper:

* *Dipolar Coupling of Nanoparticle–Molecule Assemblies: An Efficient Approach for Studying Strong Coupling*,
  Jakub Fojt, Tuomas P. Rossi, Tomasz J. Antosiewicz, Mikael Kuisma, and Paul Erhart,
  The Journal of Chemical Physics **154**, 094109 (2021); `doi:10.1063/5.0037853 <https://doi.org/10.1063/5.0037853>`_

**strongcoca** is based on Python3 and invokes functionality from the Python libraries listed below.
To support the development of free-and-open-source-software, please make sure to give proper credit to these packages when citing **strongcoca** as well.

* `ase <https://ase-lib.org>`_
* `NumPy <https://www.numpy.org>`_
* `SciPy <https://scipy.org>`_
