doped: Python toolkit for robust and repeatable charged defect supercell calculations
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910407131660288 |
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| author | Kavanagh, Seán R. Squires, Alexander G. Nicolson, Adair Mosquera-Lois, Irea Ganose, Alex M. Zhu, Bonan Brlec, Katarina Walsh, Aron Scanlon, David O. |
| author_facet | Kavanagh, Seán R. Squires, Alexander G. Nicolson, Adair Mosquera-Lois, Irea Ganose, Alex M. Zhu, Bonan Brlec, Katarina Walsh, Aron Scanlon, David O. |
| contents | Defects are a universal feature of crystalline solids, dictating the key properties and performance of many functional materials. Given their crucial importance yet inherent difficulty in measuring experimentally, computational methods (such as DFT and ML/classical force-fields) are widely used to predict defect behaviour at the atomic level and the resultant impact on macroscopic properties. Here we report doped, a Python package for the generation, pre-/post-processing, and analysis of defect supercell calculations. doped has been built to implement the defect simulation workflow in an efficient and user-friendly -- yet powerful and fully-flexible -- manner, with the goal of providing a robust general-purpose platform for conducting reproducible calculations of solid-state defect properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | doped: Python toolkit for robust and repeatable charged defect supercell calculations Kavanagh, Seán R. Squires, Alexander G. Nicolson, Adair Mosquera-Lois, Irea Ganose, Alex M. Zhu, Bonan Brlec, Katarina Walsh, Aron Scanlon, David O. Materials Science Chemical Physics Computational Physics Defects are a universal feature of crystalline solids, dictating the key properties and performance of many functional materials. Given their crucial importance yet inherent difficulty in measuring experimentally, computational methods (such as DFT and ML/classical force-fields) are widely used to predict defect behaviour at the atomic level and the resultant impact on macroscopic properties. Here we report doped, a Python package for the generation, pre-/post-processing, and analysis of defect supercell calculations. doped has been built to implement the defect simulation workflow in an efficient and user-friendly -- yet powerful and fully-flexible -- manner, with the goal of providing a robust general-purpose platform for conducting reproducible calculations of solid-state defect properties. |
| title | doped: Python toolkit for robust and repeatable charged defect supercell calculations |
| topic | Materials Science Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2403.08012 |