{ "info": { "author": "Mattias \u00c5ngqvist, William A. Mu\u00f1oz, J. Magnus Rahm, Erik Fransson, C\u00e9line Durniak, Piotr Rozyczko, Thomas Holm Rod, and Paul Erhart", "author_email": "icet@materialsmodeling.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Physics" ], "description": "icet\n====\n\n**icet** is a tool for the construction and sampling of alloy cluster\nexpansions. A detailed description of the functionality provided as well as an\nextensive tutorial can be found in the `user guide\n`_\n\n**icet** is written in Python, which allows easy integration with countless\nfirst-principles codes and analysis tools accessible from Python, and allows\nfor a simple and intuitive user interface. All computationally demanding parts\nare, however, written in C++ providing performance while maintaining\nportability. The following snippet illustrates how one can train a cluster\nexpansion:\n\n.. code-block:: python\n\n cs = ClusterSpace(primitive_cell, cutoffs, species)\n sc = StructureContainer(cs, list_of_training_structure)\n opt = Optimizer(sc.get_fit_data())\n opt.train()\n ce = ClusterExpansion(cs, opt.parameters)\n\nAfterwards the cluster expansion can be used, e.g., for finding ground state\nstructures, or sampled via Monte Carlo simulations.\n\n\nInstallation\n------------\n\nIn the most simple case, **icet** can be installed using pip as follows::\n\n pip3 install icet --user\n\nor alternatively::\n\n python3 -m pip install icet --user\n\nInstallation requires a C++11 compliant compiler. Please consult the\n`installation section of the user guide\n`_ for details.\n\n**icet** is based on Python3 and invokes functionality from other Python\nlibraries including\n`ase `_,\n`pandas `_,\n`numpy `_,\n`scipy `_,\n`scitkit-learn `_, and\n`spglib `_.\n\n\nCredits\n-------\n\n* Mattias \u00c5ngqvist\n* William A. Mu\u00f1oz\n* Magnus Rahm\n* Erik Fransson\n* C\u00e9line Durniak\n* Piotr Rozyczko\n* Thomas Holm Rod\n* Paul Erhart\n\n**icet** has been developed at Chalmers University of Technology in Gothenburg\n(Sweden) in the `Materials and Surface Theory division\n`_ at the Department of Physics, in\ncollaboration with the Data Analysis group at the `Data Management and Software\nCenter of the European Spallation Source\n`_\nin Copenhagen (Denmark).\n\nWhen using **icet** in your research please cite\n\n| M. \u00c5ngqvist, W. A. Mu\u00f1oz, J. M. Rahm, E. Fransson, C. Durniak, P. Rozyczko, T. H. Rod, and P. Erhart\n| *ICET \u2013 A Python Library for Constructing and Sampling Alloy Cluster Expansions*\n| Adv. Theory. Sim., 1900015 (2019)\n| `doi: 10.1002/adts.201900015 `_\n\n**icet** and its development are hosted on `gitlab `_. Bugs and feature requests are ideally submitted\nvia the `gitlab issue tracker `_. 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