doped: Python toolkit for robust and repeatable charged defect supercell calculations

Abstract

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, 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.