{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/9"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"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-core provides core data structures and I/O for materials science, including Element, Site, Molecule, and Structure classes with support for VASP, ABINIT, CIF, Gaussian, and other computational chemistry formats.","skillfed_tags":["materials-science","crystallography","computational-chemistry"],"use_cases":["Parse and manipulate crystal structure files from DFT calculations (VASP, ABINIT, Gaussian) for downstream analysis.","Detect crystal symmetry and perform lattice/coordinate transformations for structure comparison and matching.","Build materials science workflows that depend on standardized structure and electronic structure representations.","Access foundational utilities used by the broader pymatgen ecosystem (atomate, FireWorks, pymatgen-analysis add-ons).","Calculate bond valence sums and Ewald electrostatic energies for structure validation."],"what_it_does":"pymatgen-core is the foundational subset of the Pymatgen materials analysis library, split out to provide core data structures and I/O with minimal dependencies. It defines flexible object representations for elements, sites, molecules, and crystal structures, and handles input/output for many computational chemistry codes including VASP, ABINIT, Gaussian, and CIF files. It also includes core analysis tools such as symmetry detection, structure matching, bond valence calculations, and Ewald summation, plus electronic structure data classes for density of states and band structure.\n\nThe package is actively maintained as part of the Materials Project ecosystem and is used by thousands of researchers. It imports under the standard pymatgen namespace, so existing code continues to work. If you need higher-level analysis (phase diagrams, Pourbaix diagrams, diffusion analysis), install the full pymatgen package instead, which builds on top of pymatgen-core.","worth_installing":"Yes, if you work with crystal structures or computational materials data. pymatgen-core is actively maintained, well-documented, and widely used in materials research. It has no known vulnerabilities and permissive licensing. The 18 runtime dependencies add medium install friction, but pre-built wheels for Python 3.11\u20133.14 across major platforms mitigate this. Install the full pymatgen package instead if you need higher-level analysis modules."},"id":"pymatgen-core","links":{"html":"https://skillfed.io/packages/pymatgen-core","md":"https://skillfed.io/packages/pymatgen-core.md","pypi":"https://pypi.org/project/pymatgen-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":"MIT","license_treatment":"permissive","name":"pymatgen-core","python_support":"supports_current","summary":"Python Materials Genomics is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project (https://materialsproject.org). This repository is for pymatgen-core, which implements the core data structures and algorithms."},"popularity":{"monthly_downloads":701865,"position":5288,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.8.13"}