k-dense-ai/pymatgen
Analyze, validate, convert, and transform materials structures and computed materials data with current pymatgen APIs, including local phase diagrams, symmetry sensitivity, electronic-structure I/O, and explicitly bounded Materials Project queries.
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill pymatgen
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.
pymatgen==2026.5.4 is the latest stable wrapper release (2026-05-04).Package metadata requires Python 3.11+ and directly requires
pymatgen-core>=2026.4.16.
pymatgen-core==2026.7.16 is the latest stable core release (2026-07-16).It now contains core objects, symmetry/lattice operations, and the I/O layer,
all under the existing pymatgen.* namespace.
mp-api==0.46.4 is the latest stable Materials Project client(2026-06-15), requires Python 3.11+, and depends on
pymatgen>2024.2.20.
distributions prevents pymatgen==2026.5.4 from silently resolving to a
different future core.
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.
Molecule or periodicStructure; record lattice and periodic boundary conditions.
g/cm³, but each API's documented contract is authoritative.
Structure coordinates are fractional unlesscoords_are_cartesian=True; Molecule coordinates are Cartesian.
stoichiometry, and correction warnings; do not silently accept fixes.
guess oxidation states implicitly.
thermodynamic analysis.
symprec in Å andangle_tolerance in degrees with every assignment.
software versions, warnings, and parent/child checksums.
and round-trip-check scientifically relevant properties.
10. Build phase diagrams only from compatible total energies and correction
schemes. A computed hull is conditional on the supplied entry set.
11. Keep all database access off by default. Disclose endpoint, filters,
fields, result limit, cache behavior, output, license, and citation before
an explicit execution step.
12. Preserve an artifact manifest. Never use pickle or load an untrusted
general object graph; use schema-validated JSON and explicit constructors.
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.
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.
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.
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.
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.
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.
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.
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.
All CLIs have dependency-free --help, lazy scientific imports, bounded JSON,
and no implicit network:
scripts/composition_structure_validator.py — strict composition/structurechecks; 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
Take k-dense-ai/pymatgen from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, uv.
Without those the skill loads but fails at the first command.