### Pymatgen
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name: "pymatgen"
description: "Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science."
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| name | pymatgen |
| description | Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science. |
| license | MIT license |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
Pymatgen is a comprehensive Python library for materials analysis that powers the Materials Project. Create, analyze, and manipulate crystal structures and molecules, compute phase diagrams and thermodynamic properties, analyze electronic structure (band structures, DOS), generate surfaces and interfaces, and access Materials Project's database of computed materials. Supports 100+ file formats from various computational codes.
This skill should be used when:
# Core pymatgen
uv pip install pymatgen
# With Materials Project API access
uv pip install pymatgen mp-api
# Optional dependencies for extended functionality
uv pip install pymatgen[analysis] # Additional analysis tools
uv pip install pymatgen[vis] # Visualization tools
from pymatgen.core import Structure, Lattice
# Read structure from file (automatic format detection)
struct = Structure.from_file("POSCAR")
# Create structure from scratch
lattice = Lattice.cubic(3.84)
struct = Structure(lattice, ["Si", "Si"], [[0,0,0], [0.25,0.25,0.25]])
# Write to different format
struct.to(filename="structure.cif")
# Basic properties
print(f"Formula: {struct.composition.reduced_formula}")
print(f"Space group: {struct.get_space_group_info()}")
print(f"Density: {struct.density:.2f} g/cm³")
# Set up API key
export MP_API_KEY="your_api_key_here"
from mp_api.client import MPRester
with MPRester() as mpr:
# Get structure by material ID
struct = mpr.get_structure_by_material_id("mp-149")
# Search for materials
materials = mpr.materials.summary.search(
formula="Fe2O3",
energy_above_hull=(0, 0.05)
)
Create structures using various methods and perform transformations.
From files:
# Automatic format detection
struct = Structure.from_file("structure.cif")
struct = Structure.from_file("POSCAR")
mol = Molecule.from_file("molecule.xyz")
From scratch:
from pymatgen.core import Structure, Lattice
# Using lattice parameters
lattice = Lattice.from_parameters(a=3.84, b=3.84, c=3.84,
alpha=120, beta=90, gamma=60)
coords = [[0, 0, 0], [0.75, 0.5, 0.75]]
struct = Structure(lattice, ["Si", "Si"], coords)
# From space group
struct = Structure.from_spacegroup(
"Fm-3m",
Lattice.cubic(3.5),
["Si"],
[[0, 0, 0]]
)
Transformations:
from pymatgen.transformations.standard_transformations import (
SupercellTransformation,
SubstitutionTransformation,
PrimitiveCellTransformation
)
# Create supercell
trans = SupercellTransformation([[2,0,0],[0,2,0],[0,0,2]])
supercell = trans.apply_transformation(struct)
# Substitute elements
trans = SubstitutionTransformation({"Fe": "Mn"})
new_struct = trans.apply_transformation(struct)
# Get primitive cell
trans = PrimitiveCellTransformation()
primitive = trans.apply_transformation(struct)
Reference: See references/core_classes.md for comprehensive documentation of Structure, Lattice, Molecule, and related classes.
Convert between 100+ file formats with automatic format detection.
Using convenience methods:
# Read any format
struct = Structure.from_file("input_file")
# Write to any format
struct.to(filename="output.cif")
struct.to(filename="POSCAR")
struct.to(filename="output.xyz")
Using the conversion script:
# Single file conversion
python scripts/structure_converter.py POSCAR structure.cif
# Batch conversion
python scripts/structure_converter.py *.cif --output-dir ./poscar_files --format poscar
Reference: See references/io_formats.md for detailed documentation of all supported formats and code integrations.
Analyze structures for symmetry, coordination, and other properties.
Symmetry analysis:
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
sga = SpacegroupAnalyzer(struct)
# Get space group information
print(f"Space group: {sga.get_space_group_symbol()}")
print(f"Number: {sga.get_space_group_number()}")
print(f"Crystal system: {sga.get_crystal_system()}")
# Get conventional/primitive cells
conventional = sga.get_conventional_standard_structure()
primitive = sga.get_primitive_standard_structure()
Coordination environment:
from pymatgen.analysis.local_env import CrystalNN
cnn = CrystalNN()
neighbors = cnn.get_nn_info(struct, n=0) # Neighbors of site 0
print(f"Coordination number: {len(neighbors)}")
for neighbor in neighbors:
site = struct[neighbor['site_index']]
print(f" {site.species_string} at {neighbor['weight']:.3f} Å")
Using the analysis script:
# Comprehensive analysis
python scripts/structure_analyzer.py POSCAR --symmetry --neighbors
# Export results
python scripts/structure_analyzer.py structure.cif --symmetry --export json
Reference: See references/analysis_modules.md for detailed documentation of all analysis capabilities.
Construct phase diagrams and analyze thermodynamic stability.
Phase diagram construction:
from mp_api.client import MPRester
from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter
# Get entries from Materials Project
with MPRester() as mpr:
entries = mpr.get_entries_in_chemsys("Li-Fe-O")
# Build phase diagram
pd = PhaseDiagram(entries)
# Check stability
from pymatgen.core import Composition
comp = Composition("LiFeO2")
# Find entry for composition
for entry in entries:
if entry.composition.reduced_formula == comp.reduced_formula:
e_above_hull = pd.get_e_above_hull(entry)
print(f"Energy above hull: {e_above_hull:.4f} eV/atom")
if e_above_hull > 0.001:
# Get decomposition
decomp = pd.get_decomposition(comp)
print("Decomposes to:", decomp)
# Plot
plotter = PDPlotter(pd)
plotter.show()
Using the phase diagram script:
# Generate phase diagram
python scripts/phase_diagram_generator.py Li-Fe-O --output li_fe_o.png
# Analyze specific composition
python scripts/phase_diagram_generator.py Li-Fe-O --analyze "LiFeO2" --show
Reference: See references/analysis_modules.md (Phase Diagrams section) and references/transformations_workflows.md (Workflow 2) for detailed examples.
Analyze band structures, density of states, and electronic properties.
Band structure:
from pymatgen.io.vasp import Vasprun
from pymatgen.electronic_structure.plotter import BSPlotter
# Read from VASP calculation
vasprun = Vasprun("vasprun.xml")
bs = vasprun.get_band_structure()
# Analyze
band_gap = bs.get_band_gap()
print(f"Band gap: {band_gap['energy']:.3f} eV")
print(f"Direct: {band_gap['direct']}")
print(f"Is metal: {bs.is_metal()}")
# Plot
plotter = BSPlotter(bs)
plotter.save_plot("band_structure.png")
Density of states:
from pymatgen.electronic_structure.plotter import DosPlotter
dos = vasprun.complete_dos
# Get element-projected DOS
element_dos = dos.get_element_dos()
for element, element_dos_obj in element_dos.items():
print(f"{element}: {element_dos_obj.get_gap():.3f} eV")
# Plot
plotter = DosPlotter()
plotter.add_dos("Total DOS", dos)
plotter.show()
Reference: See references/analysis_modules.md (Electronic Structure section) and references/io_formats.md (VASP section).
Generate slabs, analyze surfaces, and study interfaces.
Slab generation:
from pymatgen.core.surface import SlabGenerator
# Generate slabs for specific Miller index
slabgen = SlabGenerator(
struct,
miller_index=(1, 1, 1),
min_slab_size=10.0, # Å
min_vacuum_size=10.0, # Å
center_slab=True
)
slabs = slabgen.get_slabs()
# Write slabs
for i, slab in enumerate(slabs):
slab.to(filename=f"slab_{i}.cif")
Wulff shape construction:
from pymatgen.analysis.wulff import WulffShape
# Define surface energies
surface_energies = {
(1, 0, 0): 1.0,
(1, 1, 0): 1.1,
(1, 1, 1): 0.9,
}
wulff = WulffShape(struct.lattice, surface_energies)
print(f"Surface area: {wulff.surface_area:.2f} Ų")
print(f"Volume: {wulff.volume:.2f} ų")
wulff.show()
Adsorption site finding:
from pymatgen.analysis.adsorption import AdsorbateSiteFinder
from pymatgen.core import Molecule
asf = AdsorbateSiteFinder(slab)
# Find sites
ads_sites = asf.find_adsorption_sites()
print(f"On-top sites: {len(ads_sites['ontop'])}")
print(f"Bridge sites: {len(ads_sites['bridge'])}")
print(f"Hollow sites: {len(ads_sites['hollow'])}")
# Add adsorbate
adsorbate = Molecule("O", [[0, 0, 0]])
ads_struct = asf.add_adsorbate(adsorbate, ads_sites["ontop"][0])
Reference: See references/analysis_modules.md (Surface and Interface section) and references/transformations_workflows.md (Workflows 3 and 9).
Programmatically access the Materials Project database.
Setup:
export MP_API_KEY="your_key_here"Search and retrieve:
from mp_api.client import MPRester
with MPRester() as mpr:
# Search by formula
materials = mpr.materials.summary.search(formula="Fe2O3")
# Search by chemical system
materials = mpr.materials.summary.search(chemsys="Li-Fe-O")
# Filter by properties
materials = mpr.materials.summary.search(
chemsys="Li-Fe-O",
energy_above_hull=(0, 0.05), # Stable/metastable
band_gap=(1.0, 3.0) # Semiconducting
)
# Get structure
struct = mpr.get_structure_by_material_id("mp-149")
# Get band structure
bs = mpr.get_bandstructure_by_material_id("mp-149")
# Get entries for phase diagram
entries = mpr.get_entries_in_chemsys("Li-Fe-O")
Reference: See references/materials_project_api.md for comprehensive API documentation and examples.
Set up calculations for various electronic structure codes.
VASP input generation:
from pymatgen.io.vasp.sets import MPRelaxSet, MPStaticSet, MPNonSCFSet
# Relaxation
relax = MPRelaxSet(struct)
relax.write_input("./relax_calc")
# Static calculation
static = MPStaticSet(struct)
static.write_input("./static_calc")
# Band structure (non-self-consistent)
nscf = MPNonSCFSet(struct, mode="line")
nscf.write_input("./bandstructure_calc")
# Custom parameters
custom = MPRelaxSet(struct, user_incar_settings={"ENCUT": 600})
custom.write_input("./custom_calc")
Other codes:
# Gaussian
from pymatgen.io.gaussian import GaussianInput
gin = GaussianInput(
mol,
functional="B3LYP",
basis_set="6-31G(d)",
route_parameters={"Opt": None}
)
gin.write_file("input.gjf")
# Quantum ESPRESSO
from pymatgen.io.pwscf import PWInput
pwin = PWInput(struct, control={"calculation": "scf"})
pwin.write_file("pw.in")
Reference: See references/io_formats.md (Electronic Structure Code I/O section) and references/transformations_workflows.md for workflow examples.
Diffraction patterns:
from pymatgen.analysis.diffraction.xrd import XRDCalculator
xrd = XRDCalculator()
pattern = xrd.get_pattern(struct)
# Get peaks
for peak in pattern.hkls:
print(f"2θ = {peak['2theta']:.2f}°, hkl = {peak['hkl']}")
pattern.plot()
Elastic properties:
from pymatgen.analysis.elasticity import ElasticTensor
# From elastic tensor matrix
elastic_tensor = ElasticTensor.from_voigt(matrix)
print(f"Bulk modulus: {elastic_tensor.k_voigt:.1f} GPa")
print(f"Shear modulus: {elastic_tensor.g_voigt:.1f} GPa")
print(f"Young's modulus: {elastic_tensor.y_mod:.1f} GPa")
Magnetic ordering:
from pymatgen.transformations.advanced_transformations import MagOrderingTransformation
# Enumerate magnetic orderings
trans = MagOrderingTransformation({"Fe": 5.0})
mag_structs = trans.apply_transformation(struct, return_ranked_list=True)
# Get lowest energy magnetic structure
lowest_energy_struct = mag_structs[0]['structure']
Reference: See references/analysis_modules.md for comprehensive analysis module documentation.
scripts/)Executable Python scripts for common tasks:
structure_converter.py: Convert between structure file formats
python scripts/structure_converter.py POSCAR structure.cifstructure_analyzer.py: Comprehensive structure analysis
python scripts/structure_analyzer.py structure.cif --symmetry --neighborsphase_diagram_generator.py: Generate phase diagrams from Materials Project
python scripts/phase_diagram_generator.py Li-Fe-O --analyze "LiFeO2"All scripts include detailed help: python scripts/script_name.py --help
references/)Comprehensive documentation loaded into context as needed:
core_classes.md: Element, Structure, Lattice, Molecule, Composition classesio_formats.md: File format support and code integration (VASP, Gaussian, etc.)analysis_modules.md: Phase diagrams, surfaces, electronic structure, symmetrymaterials_project_api.md: Complete Materials Project API guidetransformations_workflows.md: Transformations framework and common workflowsLoad references when detailed information is needed about specific modules or workflows.
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
from pymatgen.io.vasp.sets import MPRelaxSet
# Generate doped structures
base_struct = Structure.from_file("POSCAR")
dopants = ["Mn", "Co", "Ni", "Cu"]
for dopant in dopants:
trans = SubstitutionTransformation({"Fe": dopant})
doped_struct = trans.apply_transformation(base_struct)
# Generate VASP inputs
vasp_input = MPRelaxSet(doped_struct)
vasp_input.write_input(f"./calcs/Fe_{dopant}")
# 1. Relaxation
relax = MPRelaxSet(struct)
relax.write_input("./1_relax")
# 2. Static (after relaxation)
relaxed = Structure.from_file("1_relax/CONTCAR")
static = MPStaticSet(relaxed)
static.write_input("./2_static")
# 3. Band structure (non-self-consistent)
nscf = MPNonSCFSet(relaxed, mode="line")
nscf.write_input("./3_bandstructure")
# 4. Analysis
from pymatgen.io.vasp import Vasprun
vasprun = Vasprun("3_bandstructure/vasprun.xml")
bs = vasprun.get_band_structure()
bs.get_band_gap()
# 1. Get bulk energy
bulk_vasprun = Vasprun("bulk/vasprun.xml")
bulk_E_per_atom = bulk_vasprun.final_energy / len(bulk)
# 2. Generate and calculate slabs
slabgen = SlabGenerator(bulk, (1,1,1), 10, 15)
slab = slabgen.get_slabs()[0]
MPRelaxSet(slab).write_input("./slab_calc")
# 3. Calculate surface energy (after calculation)
slab_vasprun = Vasprun("slab_calc/vasprun.xml")
E_surf = (slab_vasprun.final_energy - len(slab) * bulk_E_per_atom) / (2 * slab.surface_area)
E_surf *= 16.021766 # Convert eV/Ų to J/m²
More workflows: See references/transformations_workflows.md for 10 detailed workflow examples.
Structure.from_file() handles most formatsIStructure when structure shouldn't changeSpacegroupAnalyzer to reduce to primitive cellfrom_file() and to() are preferredas_dict()/from_dict() for version-safe storagewith MPRester() as mpr:MPRelaxSet, MPStaticSet over manual INCARTransformedStructure for provenancePymatgen uses atomic units throughout:
Convert units using pymatgen.core.units when needed.
Pymatgen integrates seamlessly with:
Import errors: Install missing dependencies
uv pip install pymatgen[analysis,vis]
API key not found: Set MP_API_KEY environment variable
export MP_API_KEY="your_key_here"
Structure read failures: Check file format and syntax
# Try explicit format specification
struct = Structure.from_file("file.txt", fmt="cif")
Symmetry analysis fails: Structure may have numerical precision issues
# Increase tolerance
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
sga = SpacegroupAnalyzer(struct, symprec=0.1)
This skill is designed for pymatgen 2024.x and later. For the Materials Project API, use the mp-api package (separate from legacy pymatgen.ext.matproj).
Requirements:
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
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Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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pymatgen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend pymatgen for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
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