doped: a python package for solid-state defect and dopant calculations

Point defects are a universal feature of crystalline materials. Computational methods (DFT, quantum embedding, GW...) are widely used to predict defect behavior, before combining and comparing predictions with experimental measurements. However, there are many critical stages in the computational workflow for defects, which, when performed manually, not only leave room for human error but also consume significant researcher time and effort. Moreover, there are growing efforts to perform high-throughput investigations of defects in solids, necessitating robust, user-friendly and efficient software implementing this calculation workflow.

Here we report doped, our python package for the full generation, calculation setup, post-processing and analysis of defect supercell calculations.^2–5 The generation and thermodynamic analysis (i.e. defect formation energy diagrams, chemical potentials & stability region, doping analysis etc.) are agnostic to the underlying first-principles software, while input file generation is supported for several of the most widely-used DFT codes, including VASP, FHI-aims, CP2k, Quantum Espresso and CASTEP. A defect charge state prediction algorithm is implemented, which is shown to significantly outperform previous oxidation-state approaches in terms of both efficiency and completeness. Moreover, doped is built to be compatible with other computational toolkits for advanced defect characterisation, including ShakeNBreak⁶ for defect structure-searching, py-sc-fermi⁷ for in-depth concentration and Fermi level analysis, and CarrierCapture.jl⁸/nonrad⁹ for non-radiative recombination calculations. Its object-oriented python framework make it readily-usable in high-throughput architectures such as atomate(2) or AiiDA, with examples included in the documentation.

We will discuss the key features of doped for computational defect workflows, exemplified with relevant systems (CdTe, t-Se, Y₂Ti₂O₅S₂). We anticipate that doped will serve as a highly useful tool for computational defect researchers, being an efficient platform for conducting reproducible calculations of solid-state defect properties.