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Code used to build the defects dataset for the publication "Machine-learning structural reconstructions for accelerated point defect calculations"

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defects_workflow

Workflow to run defect calculations with aiida.

Currently, it automates the following steps:

  1. Relaxation of host structure (from mp-id or user defined structure)
  2. Defect generation
    • Defect charge states are determined based on the most common oxidation states for the element (following the strategy implemented in defectivator by Dr Alex Squires)
  3. Screening of symmetry inequivalent interstitials. This is done by relaxing the neutral state of all the symmetry inequivalent configurations for a given interstitial. The following cases are filtered out:
    • Configurations that lead to the same final structures (only one is used for later calculations)
    • Configurations very high in energy compared to the most stable one (e.g. if > 1 eV)
  4. Structure searching using shakenbreak and submission of calculations

Installation

  1. Crate conda environment (python 3.10)

  2. Install aiida-core using the system-wide installation and using pip rather than conda.

  3. Install other dependencies, including aiida-archer2-scheduler (to use the HPC archer2), parsevasp, aiida-vasp, aiida-user-addons and defectivator:

git clone git@github.com:SMTG-UCL/aiida-archer2-scheduler.git
cd aiida-archer2-scheduler
pip install -e ./
reentry scan -r aiida
git clone https://github.com/aiida-vasp/parsevasp.git
cd parsevasp
git checkout develop
cd ../
pip install -e ./parsevasp
git clone https://github.com/aiida-vasp/aiida-vasp.git
cd aiida-vasp
git checkout develop
cd ../
pip install -e ./aiida-vasp
git clone https://github.com/SMTG-UCL/aiida-user-addons.git
cd aiida-user-addons
git checkout dev
cd ../
pip install -e ./aiida-user-addons
git clone https://github.com/alexsquires/defectivator.git
cd defectivator
git checkout dev
cd ../
pip install ./defectivator

Run pip install reentry and reentry scan -r aiida

  1. Configure aiida-vasp (potcars)

  2. Install shakenbreak

git clone https://github.com/SMTG-UCL/shakenbreak.git
pip install .
  1. Install defects_workflow
git clone https://github.com/ireaml/defects_workflow.git
pip install .
  1. Add ab-initio codes to aiida profile

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Code used to build the defects dataset for the publication "Machine-learning structural reconstructions for accelerated point defect calculations"

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