
Pymatgen
FreeAnalyze and transform materials data with precision.
Free · Opens the source repo
What Pymatgen does
Pymatgen is a powerful tool designed for researchers and developers working with materials science. It provides a comprehensive set of functionalities for analyzing, validating, converting, and transforming materials structures and computed data. By leveraging the current pymatgen APIs, users can handle various tasks including the generation of local phase diagrams, symmetry analysis, and electronic-structure I/O. The skill emphasizes provenance preservation, ensuring that every operation retains a clear record of methods and parameters used.
The skill is particularly useful for those who need to work with compositions, periodic structures, and electronic structures. It allows users to treat each parse, conversion, and transformation as dependent on specific methods and parameters, which is crucial for maintaining accuracy in scientific computations. The skill also includes a robust validation process to ensure that the input data adheres to expected formats and standards, reducing the likelihood of errors in subsequent analyses.
Pymatgen is suitable for material scientists, chemists, and engineers who require a reliable framework for manipulating materials data. It supports a variety of file formats and provides tools for symmetry analysis and phase diagram generation, making it an essential resource for anyone involved in computational materials science. Additionally, the skill's focus on explicit warnings and detailed reporting helps users make informed decisions about their data transformations and analyses.
However, users should be cautious when handling untrusted input files, as there have been vulnerabilities in the past related to CIF parsing. It is recommended to use this skill in a controlled environment to mitigate risks associated with processing potentially malicious data.
When to use it
Use this skill when you need to analyze material structures, validate data, or convert between different file formats in materials science.
When not to use it
This skill may not be suitable for general-purpose data manipulation outside of materials science or for users without a background in the field.
What you can build with it
Analyzing Material Structures
Use Pymatgen to validate and analyze complex material structures, ensuring all parameters are correctly recorded.
Generating Phase Diagrams
Create local phase diagrams from computed entries, ensuring all energy data is compatible and provenance is maintained.
Converting Between Formats
Convert structure files between different formats while preserving critical information and acknowledging any potential data loss.
How to install Pymatgen
View source1. Install with the skills CLI
npx skills add k-dense-ai/scientific-agent-skills/pymatgen --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by k-dense-aipymatgen
Use pymatgen for explicit, provenance-preserving work with compositions, molecules, periodic structures, computed entries, symmetry, phase diagrams, electronic structures, and electronic-structure-code files. Treat every parse, conversion, symmetry assignment, transformation, and database result as method- and parameter-dependent.
The MIT frontmatter license covers this skill. pymatgen and
pymatgen-core are MIT; mp-api declares BSD-3-Clause-LBNL. Materials Project
data is generally CC BY 4.0, while contributed data remains owned by its
contributors. Check the exact artifact and data terms before redistribution.
Verified snapshot (2026-07-23)
pymatgen==2026.5.4is the latest stable wrapper release (2026-05-04). Package metadata requires Python 3.11+ and directly requirespymatgen-core>=2026.4.16.pymatgen-core==2026.7.16is the latest stable core release (2026-07-16). It now contains core objects, symmetry/lattice operations, and the I/O layer, all under the existingpymatgen.*namespace.mp-api==0.46.4is the latest stable Materials Project client (2026-06-15), requires Python 3.11+, and depends onpymatgen>2024.2.20.- The current API site is built from 2026.7.16 core documentation. Pinning both
distributions prevents
pymatgen==2026.5.4from silently resolving to a different future core. - Pymatgen uses date-based versions. PyPI renders the date with dots; do not infer semantic-version compatibility from the numbers.
Create a project lock for reproducibility:
uv init --python 3.11
uv add "pymatgen==2026.5.4" "pymatgen-core==2026.7.16" "mp-api==0.46.4"
uv lock
uv sync --frozen
For a disposable reviewed environment:
uv venv --python 3.11 .venv-pymatgen
uv pip install --python .venv-pymatgen/bin/python \
"pymatgen==2026.5.4" "pymatgen-core==2026.7.16" "mp-api==0.46.4"
Direct pins do not freeze all transitive wheels. Preserve uv.lock, platform,
Python version, package versions, and artifact hashes.
Required workflow
- State whether the object is a non-periodic
Moleculeor periodicStructure; record lattice and periodic boundary conditions. - State units. Pymatgen commonly uses Å, degrees, eV, eV/atom, amu, and g/cm³, but each API's documented contract is authoritative.
- State coordinate mode.
Structurecoordinates are fractional unlesscoords_are_cartesian=True;Moleculecoordinates are Cartesian. - Inspect every parser warning. For CIF, preserve occupancy, site-merging, stoichiometry, and correction warnings; do not silently accept fixes.
- Report disorder/partial occupancies and oxidation-state decoration. Never guess oxidation states implicitly.
- Run validation before symmetry, neighbor, transformation, conversion, or thermodynamic analysis.
- Sweep symmetry tolerances and report
symprecin Å andangle_tolerancein degrees with every assignment. - Treat transformations as new artifacts. Preserve the input, parameters, software versions, warnings, and parent/child checksums.
- Before conversion, identify representation loss. Write only to a new path and round-trip-check scientifically relevant properties.
- Build phase diagrams only from compatible total energies and correction schemes. A computed hull is conditional on the supplied entry set.
- Keep all database access off by default. Disclose endpoint, filters, fields, result limit, cache behavior, output, license, and citation before an explicit execution step.
- Preserve an artifact manifest. Never use pickle or load an untrusted general object graph; use schema-validated JSON and explicit constructors.
Core objects
Use the public convenience imports:
from pymatgen.core import Composition, Element, Lattice, Molecule, Structure
composition = Composition("LiFePO4", strict=True)
iron = Element("Fe")
lattice = Lattice.cubic(5.64) # Å
structure = Structure(
lattice,
["Na", "Cl"],
[[0, 0, 0], [0.5, 0.5, 0.5]],
coords_are_cartesian=False,
validate_proximity=True,
)
molecule = Molecule(
["O", "H", "H"],
[[0.0, 0.0, 0.0], [0.758, 0.0, 0.504], [-0.758, 0.0, 0.504]],
charge=0,
spin_multiplicity=1,
)
Structure and Molecule are mutable; use IStructure/IMolecule or an
explicit copy when mutation would compromise provenance. See
core classes.
Safe local structure intake
Prefer the bundled validator, which captures CIF and Python warnings and reports units, occupancy, disorder, oxidation states, periodicity, coordinate mode, and minimum distances:
python scripts/composition_structure_validator.py composition "Fe2O3"
python scripts/composition_structure_validator.py structure structure.cif
python scripts/structure_analyzer.py structure.cif --symmetry
For direct CIF work, use the current parser method and inspect both warning channels:
import warnings
from pymatgen.io.cif import CifParser
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
parser = CifParser("input.cif", check_cif=True)
structures = parser.parse_structures(
primitive=False,
check_occu=True,
on_error="raise",
)
parser_messages = list(parser.warnings)
python_messages = [str(item.message) for item in caught]
Do not parse untrusted files in a privileged process. A critical malicious-CIF code-execution flaw affected pymatgen through 2024.2.8 and was fixed in 2024.2.20; the pinned release is newer, but parsers still process attacker controlled input. Use isolation and CPU/RAM/disk/time limits.
Symmetry
Space-group assignment depends on tolerances and structure quality:
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
analyzer = SpacegroupAnalyzer(
structure,
symprec=0.01, # Å
angle_tolerance=5.0, # degrees
)
symbol = analyzer.get_space_group_symbol()
number = analyzer.get_space_group_number()
The Materials Project pipeline commonly uses symprec=0.1 Å, while pymatgen's
documented default is 0.01 Å; these can produce different assignments.
Generate a sensitivity report instead of changing tolerance until a preferred
answer appears:
python scripts/symmetry_sensitivity_report.py structure.cif \
--symprec 0.001,0.01,0.1 --angle-tolerance 1,5
See analysis modules.
Conversion and parser/writer I/O
Plan first; the planner does not open files or import pymatgen:
python scripts/io_conversion_plan.py \
--input input.cif --input-format cif \
--output POSCAR.new --output-format poscar \
--periodic --coordinate-mode direct
Then convert to a new path with explicit loss acknowledgement:
python scripts/structure_converter.py input.cif POSCAR.new \
--output-format poscar --coordinate-mode direct --allow-lossy \
--acknowledge-parser-warnings
CIF, POSCAR, XYZ, and JSON do not preserve the same semantics. Check lattice, periodicity, coordinate mode, species ordering, selective dynamics, site properties, oxidation states, labels, and disorder after every conversion. See I/O formats.
Transformations and provenance
Transform a copy and preserve history:
from pymatgen.alchemy.materials import TransformedStructure
from pymatgen.transformations.standard_transformations import (
SubstitutionTransformation,
SupercellTransformation,
)
tracked = TransformedStructure(structure.copy(), [])
tracked.append_transformation(SupercellTransformation([2, 2, 2]))
tracked.append_transformation(SubstitutionTransformation({"Na": "K"}))
derived = tracked.final_structure
history = tracked.history
One-to-many ordering, doping, slab, and magnetic transformations can expand combinatorially or invoke optional executables. Bound candidates, sites, supercell size, runtime, and output count. See transformations and workflows.
Local phase diagrams
The bundled generator is offline and accepts only a strict JSON schema with total eV per entry and provenance:
{
"schema_version": "1.0",
"energy_unit": "eV",
"energy_basis": "total_per_entry",
"provenance": {
"source": "reviewed local calculations",
"method": "one compatible energy/correction scheme"
},
"entries": [
{
"entry_id": "local-Li",
"composition": "Li",
"energy_eV": -1.0,
"provenance": {"source": "calculation manifest sha256:..."}
}
]
}
python scripts/phase_diagram_generator.py entries.json --analyze Li2O
Elemental endpoints and all competing phases must be present. Do not mix raw energies from different functionals, pseudopotentials, magnetic states, or correction conventions. Computed on-hull status is not experimental stability.
Band structures, DOS, VASP, and Q-Chem
Parse only the data needed:
from pymatgen.io.vasp import Vasprun
run = Vasprun(
"vasprun.xml",
parse_dos=True,
parse_eigen=True,
parse_projected_eigen=False,
parse_potcar_file=False,
)
band_structure = run.get_band_structure(line_mode=True)
band_gap = band_structure.get_band_gap()
complete_dos = run.complete_dos
Projected eigenvalues can require extreme memory. Verify convergence, k-path, spin/SOC settings, Fermi-level conventions, smearing, and projection basis before interpreting gaps or DOS. A parser success is not a converged calculation.
Current Q-Chem interfaces are pymatgen.io.qchem.inputs.QCInput and
pymatgen.io.qchem.outputs.QCOutput:
from pymatgen.io.qchem.inputs import QCInput
job = QCInput(
molecule,
rem={"job_type": "sp", "method": "wb97x-v", "basis": "def2-svpd"},
)
text = str(job)
Pymatgen writes inputs and parses outputs; it does not grant a VASP or Q-Chem license or establish method validity. POTCAR files are VASP-licensed and are not distributed by pymatgen. Never redistribute them or scan unrelated directories for them. Optional tools such as enumlib, Bader, packmol, ffmpeg, and Zeo++ are native/external executables: review provenance, licenses, argv, working directory, and resource limits before a separate explicit invocation.
Materials Project: plan before network
Use only:
from mp_api.client import MPRester
The client reads MP_API_KEY when constructed. Supply only that named
environment variable through the user's shell or secret manager. Do not accept
the key as a CLI argument, traverse .env files, dump environment variables,
or print exception data without redaction.
Dry-run planning is the default:
python scripts/mp_query.py \
--chemsys Li-Fe-O \
--energy-above-hull 0 0.05 \
--fields formula_pretty,energy_above_hull,band_gap,origins \
--limit 25
Only --execute permits one bounded summary query and requires a new output:
python scripts/mp_query.py \
--material-id mp-149 \
--fields formula_pretty,structure,origins,last_updated \
--limit 1 --output mp-149.json --execute
The CLI sets num_chunks=1, requires explicit fields and filters, caps results,
does not implement an implicit result cache, and never overwrites output.
MPRester initialization also performs compatibility/heartbeat metadata
requests; the plan discloses these, disables the platform-detail user agent and
local database-version notification log, and records the returned database
version. The summary workflow does not request full-dataset cache downloads.
mp-api 0.46.4 retries HTTP 429/502/504 according to its own configured policy
and respects Retry-After; do not invent a numeric service quota or add an
unbounded retry loop.
Materials Project core values are computed, method-dependent data—not experimental truth. PBE commonly overestimates lattice parameters and systematically underestimates band gaps; aggregated values can change across database releases. Preserve retrieval time, query, fields, material/task origins, database release when available, client versions, CC BY attribution, and the canonical plus property-specific citations. See Materials Project API.
Bundled CLIs
All CLIs have dependency-free --help, lazy scientific imports, bounded JSON,
and no implicit network:
scripts/composition_structure_validator.py— strict composition/structure checks; optional oxidation-state guessing is explicit and bounded.scripts/structure_analyzer.py— bounded lattice, sites, symmetry, distance, and optional CrystalNN report.scripts/symmetry_sensitivity_report.py— tolerance-grid space groups.scripts/io_conversion_plan.py— dependency-free representation-loss plan.scripts/structure_converter.py— one-file conversion to a new path.scripts/phase_diagram_generator.py— strict local computed-entry hull.scripts/mp_query.py— dry-run MP query plan and opt-in bounded client.scripts/artifact_manifest.py— checksums, versions, sources, and provenance.
Use:
python scripts/artifact_manifest.py \
--artifact input.cif --artifact analysis.json \
--workflow "local symmetry sensitivity" --output manifest.json
References
- Core classes
- I/O formats, VASP, and Q-Chem
- Analysis, symmetry, phase diagrams, bands, and DOS
- Transformations and workflows
- Materials Project API, provenance, license, and limits
Sources (verified 2026-07-23)
- pymatgen 2026.5.4 on PyPI
- pymatgen-core 2026.7.16 on PyPI
- pymatgen API documentation
- pymatgen changelog
- mp-api 0.46.4 on PyPI
- Materials Project API getting started
- Materials Project query guide
- Materials Project FAQ and computed-data caveats
- Materials Project citation page
- Official tutorial series endorsed by pymatgen
Frequently asked questions about Pymatgen
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