doped
doped is a Python package for managing solid-state defect calculations, with functionality to
generate defect structures and relevant competing phases (for chemical potentials), interface with
ShakeNBreak for defect structure-searching, write VASP input files for defect supercell calculations,
and automatically parse and analyse the results.
Example Outputs:
Chemical potential/stability region plots and defect formation energy (a.k.a. transition level) diagrams:
Tutorials showing the code functionality and usage are provided on the Tutorials page.
doped can be installed via PyPI (pip install doped) or conda if preferred, and further
instructions for setting up POTCAR files with pymatgen (needed for input file generation), if not
already done, are provided on the Installation page.
ShakeNBreak
As shown in the tutorials, it is highly recommended to use the ShakeNBreak approach when calculating point defects in solids, to ensure you have identified the ground-state structures of your defects. As detailed in the theory paper, skipping this step can result in drastically incorrect formation energies, transition levels, carrier capture (basically any property associated with defects). This approach is followed in the tutorials, with a more in-depth explanation and tutorial given on the ShakeNBreak docs.
Summary GIF:
SnB CLI Usage:
Acknowledgements
doped (née DefectsWithTheBoys #iykyk) has benefitted from feedback from many
users, in particular members of the Scanlon and
Walsh research groups who are using it in
their work. Direct contributors are listed in the GitHub Contributors sidebar; including Seán
Kavanagh, Bonan Zhu, Katarina Brlec, Adair Nicolson, Sabrine Hachmioune and Savya Aggarwal.
Code to efficiently identify defect species from input supercell structures was contributed by
Alex Ganose, and the colour scheme for defect formation energy plots was originally templated from
the aide package, developed by Adam Jackson and Alex Ganose.
The docs website setup was templated from the ShakeNBreak docs set up by Irea Mosquera-Lois 🙌
doped was originally based on the excellent PyCDT (no longer maintained), but transformed and
morphed over time as more and more functionality was added. After breaking changes in pymatgen, the
package was rewritten to operate using the new pymatgen-analysis-defects package.
Studies using doped
A. T. J. Nicolson et al. Journal of Materials Chemistry A 2023
Y. W. Woo, Z. Li, Y-K. Jung, J-S. Park, A. Walsh ACS Energy Letters 2023
P. A. Hyde et al. Inorganic Chemistry 2023
J. Willis, K. B. Spooner, D. O. Scanlon. ChemRxiv 2023
X. Wang et al. arXiv 2023
J. Cen et al. Journal of Materials Chemistry A 2023
J. Willis & R. Claes et al. ChemRxiv 2023
I. Mosquera-Lois & S. R. Kavanagh, A. Walsh, D. O. Scanlon npj Computational Materials 2023
Y. T. Huang & S. R. Kavanagh et al. Nature Communications 2022
S. R. Kavanagh, D. O. Scanlon, A. Walsh, C. Freysoldt Faraday Discussions 2022
S. R. Kavanagh, D. O. Scanlon, A. Walsh ACS Energy Letters 2021
C. J. Krajewska et al. Chemical Science 2021