**doped** is a Python toolkit designed for generating, pre- and post-processing, and analyzing defect supercell calculations in materials science. The software aims to provide a robust, user-friendly, and efficient platform for conducting reproducible calculations of solid-state defect properties. Key features include: - **Supercell Generation**: Optimizes the choice of supercells to maximize the minimum distance between periodic images, reducing finite-size errors and computational costs. - **Charge-State Estimation**: Enhances the accuracy and completeness of charge state estimation by incorporating oxidation state probabilities, electronic states of the host crystal, and charge state magnitudes. - **Competing Phase Selection**: Efficiently identifies and calculates the total energies of competing phases bordering the host compound, reducing the need for extensive high-throughput calculations. - **Automated Symmetry & Degeneracy Handling**: Automatically determines point symmetry and computes degeneracy factors, which are crucial for defect concentration calculations. - **Thermodynamic Analysis**: Provides tools for analyzing defect thermodynamics, including formation energy diagrams, Fermi level solving, and doping analysis. - **Finite-Size Corrections**: Implementations of Freysoldt (FNV) and Kumagai (eFNV) image charge corrections for accurate defect properties. - **Reproducibility & Tabulation**: Supports reproducible analysis by saving all input parameters and results to JSON files, facilitating data sharing and analysis. - **High-Throughput Compatibility**: Designed to be compatible with high-throughput architectures, such as atomate and AiDA, and integrates seamlessly with other computational toolkits for advanced defect characterization. **doped** has been used in several publications to manage the defect simulation workflow, demonstrating its utility and reliability in computational defect modeling. The software is available under the Creative Commons Attribution 4.0 International License and is maintained by a team of researchers from various institutions.**doped** is a Python toolkit designed for generating, pre- and post-processing, and analyzing defect supercell calculations in materials science. The software aims to provide a robust, user-friendly, and efficient platform for conducting reproducible calculations of solid-state defect properties. Key features include: - **Supercell Generation**: Optimizes the choice of supercells to maximize the minimum distance between periodic images, reducing finite-size errors and computational costs. - **Charge-State Estimation**: Enhances the accuracy and completeness of charge state estimation by incorporating oxidation state probabilities, electronic states of the host crystal, and charge state magnitudes. - **Competing Phase Selection**: Efficiently identifies and calculates the total energies of competing phases bordering the host compound, reducing the need for extensive high-throughput calculations. - **Automated Symmetry & Degeneracy Handling**: Automatically determines point symmetry and computes degeneracy factors, which are crucial for defect concentration calculations. - **Thermodynamic Analysis**: Provides tools for analyzing defect thermodynamics, including formation energy diagrams, Fermi level solving, and doping analysis. - **Finite-Size Corrections**: Implementations of Freysoldt (FNV) and Kumagai (eFNV) image charge corrections for accurate defect properties. - **Reproducibility & Tabulation**: Supports reproducible analysis by saving all input parameters and results to JSON files, facilitating data sharing and analysis. - **High-Throughput Compatibility**: Designed to be compatible with high-throughput architectures, such as atomate and AiDA, and integrates seamlessly with other computational toolkits for advanced defect characterization. **doped** has been used in several publications to manage the defect simulation workflow, demonstrating its utility and reliability in computational defect modeling. The software is available under the Creative Commons Attribution 4.0 International License and is maintained by a team of researchers from various institutions.