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PyCDT

Python Charge Defects Toolkit (PyCDT) is a python package aimed at making charged defects modeling simpler, high throughput ready, and also accessible to researchers who don't have the required background. PyCDT can handle thermodynamic calculations and error corrections in the context of periodic boundary condition density functional calculations of charged defects in semiconductors and insulators. It can also generate the inputs for required DFT calculations and can process the output of the DFT calculations. The code is modular and any DFT code can be integrated into PyCDT for defect calculations.

Requirements

PyCDT requires pymatgen (and its dependencies) and optionally sxdefectalign packages.

Source Code

If not available already, use the following steps.

  1. Install git, if not already packaged with your system.

  2. Download the pycdt source code using the command:

    git clone https://bitbucket.org/mbkumar/pycdt.git

Installation

  1. (Optional step, but strongly suggested) Download and install anaconda and create a new virtual environment. For example, to create a new anaconda based virtual environment with a name pycdt_venv, run the following steps.:

    conda create -n pycdt_venv
conda install -n pycdt_venv matplotlib numpy scipy setuptools

    Activate this virtual environment, by running the command:

    conda activate pycdt_venv

    If everything goes well, you should see pycdt_venv at the command line prompt. Don't forget to activate your virtual environment whenever you are trying to update or use pycdt.

  2. Navigate to pycdt root directory:

    cd pycdt

  3. Install the code, using the command:

    python setup.py install

    The command tries to obtain the required packages and their dependencies and install them automatically. (Access to root may be needed if virtualenv is not used.) You can run the pycdt now.

  4. (Optional for power users) The package can be installed at non-standard locations using the command:

    python setup.py install --prefix PYCDT_INSTALL_DIR

    where PYCDT_INSTALL_DIR is your choice of directory. In UNIX/Linux environments, add PYCDT_INSTALL_DIR to PATH and PYTHONPATH variables by the following commands:

    export PATH=$PATH:PYCDT_INSTALL_DIR
export PYTHONPATH=$PYTHONPATH:PYCDT_INSTALL_DIR

  5. Configure your VASP pseudopotentials for use with pymatgen.:

    pmg config -p <EXTRACTED_VASP_POTCAR> <MY_PSP>

    Here <EXTRACTED_VASP_POTCAR> is the folder where your pseudopotentials are present and <MY_PSP> is the directory where the layout of pseudopotentials is organized by pymatgen. For more information refer to pymatgen installation instructions.

  6. Set the VASP pseudopotential directory in $HOME/.pmgrc.yaml using the command:

    pmg config --add PMG_VASP_PSP_DIR <MY_PSP>

    where <MY_PSP> is the directory used in the previous step.

  7. Set the Materials Project API key in $HOME/.pmgrc.yaml using the command:

    pmg config -add PMG_MAPI_KEY <Your mapi key obtained from www.materialsproject.org>

Documentation

For help on how to use PyCDT, read the article published in Computer Physics Communication.

Examples

From the pycdt root folder, go to examples folder by typing:

cd examples

Questions?

Post your questions on PyCDT forum.

Citation

To support PyCDT development, if you use pycdt in your research, please cite the following Comp Phys Comm article.

  • Danny Broberg, Bharat Medasani, Nils E.R. Zimmermann, Guodong Yu, Andrew Canning, Maciej Haranczyk, Mark Asta, Geoffroy Hautier, PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators, Computer Physics Communications, Volume 226, 2018, Pages 165-179.