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        "author": "Ole Sch\u00fctt, Edward Ditler, Aliaksandr Yakutovich, Patrick Seewald, Tiziano M\u00fcller, Andreas Gl\u00f6ss, Leonid Kahle",
        "author_email": "ole.schuett@cp2k.org",
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        "classifiers": [
            "Development Status :: 4 - Beta",
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            "Programming Language :: Python :: 2.7"
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        "description": "# AiiDA CP2K\n**The CP2K plugin for the AiiDA workflow and provenance engine.**\n\n\n![logo](./aiida-cp2k_logo.png)\n\n# Installation\n\nIf you use `pip`, you can install it as: \n```\npip install aiida-cp2k\n```\n\n\n# Features\nFollowing the philosophy to _''enable without getting in the way''_, this plugin provides access to all of CP2K's capabilities through a small set of well-tested features:\n\n- A full [CP2K input](https://manual.cp2k.org) has to be provided as a nested Python dictionary ([example](./test/test_dft.py)):\n```\nparams = {'FORCE_EVAL': {'METHOD': 'Quickstep', 'DFT': { ... }}}\ncalc.use_parameters(ParameterData(dict=params))\n```\n\n- Section parameters are stored as key `_` in the dictionary:\n```\nxc_section = {'XC_FUNCTIONAL': {'_': 'LDA'}}\n```\n\n- Repeated sections are stored as a list:\n```\nkind_section = [{'_': 'H', 'BASIS_SET': 'DZVP-MOLOPT-GTH', 'POTENTIAL': 'GTH-LDA'},\n                {'_': 'O', 'BASIS_SET': 'DZVP-MOLOPT-GTH', 'POTENTIAL': 'GTH-LDA'}]\n```\n\n- Most data files (basis sets, pseudo potentials, VdW, etc.) are auto-discovered from CP2K's [data directory](https://github.com/cp2k/cp2k/tree/master/cp2k/data).\n```\ndft_section = {'BASIS_SET_FILE_NAME': 'BASIS_MOLOPT', ...}\n```\n\n- Additional data files can be added as AiiDA SinglefileData ([example](test/test_mm.py)):\n```\nwater_pot = SinglefileData(file=\"/tmp/water.pot\")\ncalc.use_file(water_pot, linkname=\"water_pot\")\n```\n\n- The start geometry can be provided as AiiDA StructureData ([example](./test/test_dft.py)):\n```\natoms = ase.build.molecule('H2O', vacuum=2.0)\ncalc.use_structure(StructureData(ase=atoms))\n```\n\n- Alternatively the start geometry can be contained in the CP2K input ([example](./test/test_no_struct.py)):\n```\ncoord_section = {' ': ['H    2.0   2.0   2.737166',\n                       'H    2.0   2.0   2.000000']},\n```\n\n- For restarting a calculation a parent folder can be attached  ([example](./test/test_restart.py)):\n```\ncalc2.use_parent_folder(calc1.out.remote_folder)\n```\n\n- By default only the output and restart file (if present) are retrieved. Additional files are retrieved upon request ([example](test/test_mm.py)):\n```\nsettings = {'additional_retrieve_list': [\"*.cube\"]}\ncalc.use_settings(ParameterData(dict=settings))\n```\n\n- The final geometry is extracted from the restart file (if present) and stored in AiiDA ([example](./test/test_geopt.py)):\n```\nprint(calc.out.output_structure)\n```\n\n- From the CP2K output only the #warnings and final energy are parsed ([example](./test/test_mm.py)):\n```\nprint(calc.res.nwarnings, calc.res.energy, calc.res.energy_units)\n```\n\n- The calculation is considered failed if #warnings can not be found ([example](./test/test_failure.py)).\n\n- The conversion of geometries between AiiDA and CP2K has a precision of at least 1e-10 \u00c5ngstr\u00f6m ([example](./test/test_precision.py)).\n\n- The Python code complies with the [Flake8](http://flake8.pycqa.org) coding conventions.\n\n\n# Testing\n\nEvery commit and pull request is automatically tested by [TravisCI](https://travis-ci.org/cp2k/aiida-cp2k/).\n\nTo run the tests locally install [Docker](https://docs.docker.com/engine/installation/) and execute the following commands:\n```\ngit clone https://github.com/cp2k/aiida-cp2k\ndocker build -t aiida_cp2k_test aiida-cp2k\ndocker run -it --init aiida_cp2k_test\n```\n\n\n",
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