doped: Python toolkit for robust and repeatable chargeddefect supercell calculations
Seán R. Kavanagh, Alexander G. Squires, Adair Nicolson, Irea Mosquera‐Lois, Alex M. Ganose, Bonan Zhu, Katarina Brlec, Aron Walsh, David O. Scanlon
Abstract
Defects are a universal feature of crystalline solids, dictating the key properties and performance \nof many functional materials. Given their crucial importance yet inherent difficulty in measuring \nexperimentally, computational methods (such as DFT and ML/classical force-fields) are widely \nused to predict defect behaviour at the atomic level and the resultant impact on macroscopic \nproperties. Here we report doped, a Python package for the generation, pre-/post-processing, \nand analysis of defect supercell calculations. doped has been built to implement the defect \nsimulation workflow in an efficient and user-friendly – yet powerful and fully-flexible – manner, \nwith the goal of providing a robust general-purpose platform for conducting reproducible \ncalculations of solid-state defect properties.