pymatgen Materials Analysis

What it does

Guides work with current pymatgen APIs (pymatgen 2026.5.4 / pymatgen-core 2026.7.16) in a way that keeps every result auditable.

• Structure & composition validation: parses CIF/POSCAR and reports occupancies, disorder, oxidation-state decoration, minimum interatomic distances, and every parser warning instead of silently accepting fixes.
• Symmetry sensitivity: sweeps symprec and angle_tolerance grids so space-group assignments come with their tolerance context.
• Format conversion: produces a representation-loss plan first, writes only to a new path, and round-trip-checks the properties you care about.
• Local phase diagrams: builds convex hulls offline from a strict JSON schema that requires total energies from one compatible correction scheme.
• VASP / Q-Chem I/O: parses band structures and DOS selectively from vasprun.xml while forcing convergence, k-path, and Fermi-level checks.
• Materials Project queries: dry-run planning by default; --execute allows exactly one bounded query with explicit fields, filters, and result caps.
• Artifact manifests: checksums, versions, and sources for full provenance.

Who it's for

• Materials and chemistry researchers validating DFT-derived numbers before publication
• Graduate students building crystal-structure cleanup or conversion pipelines
• Labs consuming Materials Project data who must respect CC BY attribution and citations
• Anyone running computational workflows where reproducibility (pinned versions, provenance) matters

Examples

1. "Can I trust this CIF?" — composition_structure_validator.py structure.cif surfaces partial occupancies, site-merging warnings, and suspiciously close atom pairs in one pass.
2. "My space group disagrees with the paper" — symmetry_sensitivity_report.py structure.cif --symprec 0.001,0.01,0.1 explains the gap between the Materials Project convention (0.1 Å) and pymatgen's 0.01 Å default.
3. "Is my Li-Fe-O structure on the hull?" — collect same-scheme total energies into JSON and run phase_diagram_generator.py entries.json --analyze Li2O for hull distance and decomposition products.

Install the skill I found on Claude Skill Mart.
Copy the skills/pymatgen folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/pymatgen/.
When it's done, tell me in one line what this skill can do.