**pymatgen-analysis-defects: A Python Package for Analyzing Point Defects in Crystalline Materials** **Authors:** Jimmy-Xuan Shen and Joel Varley **Affiliation:** Lawrence Livermore National Laboratory, Livermore, California, United States **DOI:** 10.21105/joss.05941 **Statement of Need:** Point defects significantly influence the properties of semiconductor and optoelectronic materials. However, the computational cost of defect simulations is much higher than that of bulk calculations due to large simulation cells and high-density functionals. Managing and curating defect calculation results can save significant computational resources. Building a high-quality, persistent defects database can further reduce the computational cost for the entire community. **Summary:** The package **pymatgen-analysis-defects** is designed to integrate into high-throughput workflows for managing complex defect calculations. It focuses on defect analysis without relying on specific high-throughput workflow frameworks, making it a standalone tool. The package includes features for re-optimizing structures, which is crucial for building a reliable database. It also provides tools for analyzing carrier recombination, such as obtaining chemical potential contributions, finite-size corrections, optical transitions, and non-radiative recombination. **Key Features:** 1. **Automatic Defect Definition:** The package can automatically define point defects based on atomic structure and electronic charge density. 2. **Integration with atomate2:** It integrates with the atomate2 workflow framework to dynamically create and manage defect calculations. 3. **Database Management:** It supports the aggregation and extension of defect results over time, facilitating the creation of a comprehensive defects database. **References:** The package incorporates contributions from other open-source projects, including pyCDT and nonrad, and is supported by LLNL LDRD funding. **Conclusion:** **pymatgen-analysis-defects** is a comprehensive tool for analyzing point defects in crystalline materials, designed to streamline and enhance the computational process, particularly in high-throughput settings.**pymatgen-analysis-defects: A Python Package for Analyzing Point Defects in Crystalline Materials** **Authors:** Jimmy-Xuan Shen and Joel Varley **Affiliation:** Lawrence Livermore National Laboratory, Livermore, California, United States **DOI:** 10.21105/joss.05941 **Statement of Need:** Point defects significantly influence the properties of semiconductor and optoelectronic materials. However, the computational cost of defect simulations is much higher than that of bulk calculations due to large simulation cells and high-density functionals. Managing and curating defect calculation results can save significant computational resources. Building a high-quality, persistent defects database can further reduce the computational cost for the entire community. **Summary:** The package **pymatgen-analysis-defects** is designed to integrate into high-throughput workflows for managing complex defect calculations. It focuses on defect analysis without relying on specific high-throughput workflow frameworks, making it a standalone tool. The package includes features for re-optimizing structures, which is crucial for building a reliable database. It also provides tools for analyzing carrier recombination, such as obtaining chemical potential contributions, finite-size corrections, optical transitions, and non-radiative recombination. **Key Features:** 1. **Automatic Defect Definition:** The package can automatically define point defects based on atomic structure and electronic charge density. 2. **Integration with atomate2:** It integrates with the atomate2 workflow framework to dynamically create and manage defect calculations. 3. **Database Management:** It supports the aggregation and extension of defect results over time, facilitating the creation of a comprehensive defects database. **References:** The package incorporates contributions from other open-source projects, including pyCDT and nonrad, and is supported by LLNL LDRD funding. **Conclusion:** **pymatgen-analysis-defects** is a comprehensive tool for analyzing point defects in crystalline materials, designed to streamline and enhance the computational process, particularly in high-throughput settings.