{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/8"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"Pymatgen provides Python classes and analysis tools for materials science workflows, including structure representation, file I/O for computational chemistry formats (VASP, ABINIT, Gaussian, CIF), phase diagrams, electronic structure analysis, and integration with the Materials Project REST API.","skillfed_tags":["materials-science","computational-chemistry","crystal-structures"],"use_cases":["Parse and manipulate crystal structures from VASP, ABINIT, or Gaussian output files for post-processing analysis","Generate phase diagrams and Pourbaix diagrams for thermodynamic stability assessment","Analyze electronic structure data including band structures and density of states","Query and download structures and properties from the Materials Project database","Automate high-throughput computational materials workflows with standardized structure handling"],"what_it_does":"Pymatgen is a materials analysis library that provides object-oriented representations of crystalline structures, molecules, and sites, along with tools for reading and writing computational chemistry file formats (VASP, ABINIT, Gaussian, CIF, XYZ, and others). It is used by thousands of researchers and underpins the Materials Project, a large-scale materials database and analysis platform.\n\nThe library includes analysis capabilities for phase diagrams, Pourbaix diagrams, diffusion analysis, reaction pathways, and electronic structure properties like density of states and band structure. It integrates with the Materials Project REST API and is optimized for coordinate manipulations using NumPy and SciPy vectorization. The codebase is actively maintained by the Materialyze Lab, the ABINIT group, and other research teams, with continuous integration testing and a growing ecosystem of community add-ons.","worth_installing":"Yes. Pymatgen is a mature, actively maintained library with no known vulnerabilities, low install friction, and broad adoption in computational materials science. The MIT license imposes no restrictions. Install it if you work with crystal structures, computational chemistry outputs, or materials databases; skip it if your work does not involve materials analysis."},"id":"pymatgen","links":{"html":"https://skillfed.io/packages/pymatgen","md":"https://skillfed.io/packages/pymatgen.md","pypi":"https://pypi.org/project/pymatgen/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-04","license_spdx":null,"license_treatment":"permissive","name":"pymatgen","python_support":"supports_current","summary":"Python Materials Genomics is a robust materials analysis code that defines core object representations for structures"},"popularity":{"monthly_downloads":929373,"position":4705,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2026.5.4"}