Metadata-Version: 2.1
Name: dfttk
Version: 0.2.1
Summary: Density functional theory workflows for finite temperature thermodynamics based on atomate workflows. Created by the Phases Research Lab
Home-page: https://github.com/phasesresearchlab/dfttk
Author: Brandon Bocklund
Author-email: brandonbocklund@gmail.com
License: MIT
Description: =========================================
        DFTTK: Density Functional Theory Tool Kit
        =========================================
        
        **Ultimate goals:** For a given structure and elements, calculate the free energy with respect to possible internal degree of freedoms.
        
        - Features
        
         - High-throughput. It can run plenty of structures with one simple command.
         - Simple. Only the structure file is required.
        
        - The following workflows are currently implemented:
        
         - Gibbs energy workflow for stable structures
         - Minimum volume finding workflow
        
        **Note:** This repo contains the custom workflows developed by the Phases Research Lab that do not fit into the scope of the public atomate repository.
        
        
        Installation
        ============
        
        DFTTK requires Python 3. Python 2 support for NumPy ends 2019-01-01.
        
        Create virtual environment (optional)
        -------------------------------------
        
        Anaconda_ or Miniconda_ is required. (Another option is using virtualenv_)
        
        .. code-block:: bash
        
            #conda create -n ENV_NAME python=VERSION
            conda create -n dfttk python=3.6
            #Activate
            conda activate dfttk
            #Deactivate
            conda deactivate
        
        .. _virtualenv: https://github.com/pypa/virtualenv
        .. _Anaconda: https://www.anaconda.com/
        .. _Miniconda: https://docs.conda.io/en/latest/miniconda.html
        
        
        Install dfttk
        -------------
        
        To install dfttk, there are several ways.
        
        - pip
        
        From the command line, run ``pip install dfttk``
        
        - conda
        
        Anaconda packages of DFTTK are currently not supported. If you are using Anaconda, you should be able to install with pip.
        
        - development versions
        
        .. code-block:: bash
        
            git clone https://github.com/phasesresearchlab/dfttk
            cd dfttk
            pip install -e .
        
        Configuration
        =============
        
        Preparation
        -----------
        
        Prepare following files.
        
        .. code-block:: bash
        
            current_folder
            ├── psp                          [specified by -psp]
            │   ├── pseudopotential_content  [required if you didnot configurate pymatgen]
            │   └── ...
            ├── config                       [specified by -c parameter]
            │   ├── db.json                  [required]
            │   ├── my_launchpad.yaml        [required]
            │   ├── FW_config.yaml           [optional]
            │   ├── my_fworker.yaml          [optional]
            │   └── my_qadapter.yaml         [optional]
            └── vaspjob.pbs                  [optional, specified by -q parameter]
        
        
        Config
        ------
        
        After prepared above files, simply run
        
        .. code-block:: bash
        
            dfttk config -all
        
        **PRL GROUP NOTE:** If you use ACI cluster at PRL group, you can get the pseudopotentials from ACI
        
        .. code-block:: bash
        
            dfttk config -all -aci
        
        For more details, ref. Configuration_
        
        .. _Configuration: docs/Configuration.md
        
        Use
        ===
        
        1. Prepare structure file(s), e.g. POSCAR
        2. Simply run ``dfttk run -f POSCAR``
        
        For more details, ref. Get_Started_
        
        .. _Get_Started: docs/Get_started.md
        
        Contributing
        ============
        
        See CONTRIBUTING.rst_
        
        .. _CONTRIBUTING.rst: CONTRIBUTING.rst
        
        License
        -------
        
        DFTTK is MIT licensed. See LICENSE_
        
        .. _LICENSE: LICENSE
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Provides-Extra: dev
