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 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.

Band Degeneracy and Anisotropy Enhances Thermoelectric Performance from Sb2Si2Te6 to Sc2Si2Te6

“…Using HSE06 + SOC with the doped package, the stability region (chemical potential limits) of Sc 2 Si 2 Te 6 in the Sc–Si–Te chemical space was computed (Figure S1), showing it to be thermodynamically stable with a relatively large stability window, suggesting ready synthesizability as witnessed experimentally …”

“…The doped defect calculation software 45 was used to collate all competing phases in the Sc–Si–Te chemical space within 0.1 eV/atom of the convex hull according to the Materials Project database—which were then re-relaxed and calculated using HSE06 + SOC, parsed the calculation outputs, and plotted the calculated chemical stability region (chemical potential ranges) for Sc 2 Si 2 Te 6 .…”