Defect Oriented Python Environment Distribution (DOPED)

This is a (mid-development) Python package for managing solid-state defect calculations, geared toward VASP. Much of it is a modified version of the excellent PyCDT.
See this link for the original PyCDT paper.

Defect formation energy plots are templated from AIDE and follow the aesthetics philosopy of sumo, both developed by the dynamic duo Adam Jackson and Alex Ganose.

This code is still being customised, so in the spirit of efficiency and avoiding redundant work, there are example Jupyter notebooks (the .ipynb files) provided to show the code functionality and usage. (Better to open in Jupyter, after installing, rather than with GitHub preview).

Requirements

doped requires pymatgen (and its dependencies).

Installation

1. Download the doped source code using the command:

  git clone https://github.com/SMTG-UCL/doped

2. Navigate to root directory:

  cd doped

3. Install the code, using the command:

  pip install -e .

This 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.

4. (If not set) Set the VASP pseudopotential directory in $HOME/.pmgrc.yaml as follows::

  PMG_VASP_PSP_DIR: <Path to VASP pseudopotential top directory>

Within your VASP pseudopotential top directory, you should have a folder named POT_GGA_PAW_PBE which contains the POTCAR.X(.gz) files (in this case for PBE POTCARs).

(Necessary to generate POTCAR files, auto-determine INCAR settings such as NELECT for charged defects...)

5. (Optional) Set the Materials Project API key in $HOME/.pmgrc.yaml as follows::

  MAPI_KEY: <Your mapi key obtained from www.materialsproject.org>

(For pulling structures and comparing properties with the Materials Project database).

Word of Caution

There is quite possibly a couple of bugs in this code, as it is very much still experimental and in development. If you find any, please let us know!