  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.
