pymatgen▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### Pymatgen
- ›name: "pymatgen"
- ›description: "Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science."
| 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 - Python Materials Genomics
Overview
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.
When to Use This Skill
This skill should be used when:
- Working with crystal structures or molecular systems in materials science
- Converting between structure file formats (CIF, POSCAR, XYZ, etc.)
- Analyzing symmetry, space groups, or coordination environments
- Computing phase diagrams or assessing thermodynamic stability
- Analyzing electronic structure data (band gaps, DOS, band structures)
- Generating surfaces, slabs, or studying interfaces
- Accessing the Materials Project database programmatically
- Setting up high-throughput computational workflows
- Analyzing diffusion, magnetism, or mechanical properties
- Working with VASP, Gaussian, Quantum ESPRESSO, or other computational codes
Quick Start Guide
Installation
# 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
Basic Structure Operations
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")
Materials Project Integration
# 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)
)
Core Capabilities
1. Structure Creation and Manipulation
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.
2. File Format Conversion
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.
3. Structure Analysis and Symmetry
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.
4. Phase Diagrams and Thermodynamics
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.
5. Electronic Structure Analysis
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).
6. Surface and Interface Analysis
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).
7. Materials Project Database Access
Programmatically access the Materials Project database.
Setup:
- Get API key from https://next-gen.materialsproject.org/
- Set environment variable:
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.
8. Computational Workflow Setup
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.
9. Advanced Analysis
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.
Bundled Resources
Scripts (scripts/)
Executable Python scripts for common tasks:
-
structure_converter.py: Convert between structure file formats- Supports batch conversion and automatic format detection
- Usage:
python scripts/structure_converter.py POSCAR structure.cif
-
structure_analyzer.py: Comprehensive structure analysis- Symmetry, coordination, lattice parameters, distance matrix
- Usage:
python scripts/structure_analyzer.py structure.cif --symmetry --neighbors
-
phase_diagram_generator.py: Generate phase diagrams from Materials Project- Stability analysis and thermodynamic properties
- Usage:
python scripts/phase_diagram_generator.py Li-Fe-O --analyze "LiFeO2"
All scripts include detailed help: python scripts/script_name.py --help
References (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 workflows
Load references when detailed information is needed about specific modules or workflows.
Common Workflows
High-Throughput Structure Generation
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}")
Band Structure Calculation Workflow
# 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()
Surface Energy Calculation
# 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.
Best Practices
Structure Handling
- Use automatic format detection:
Structure.from_file()handles most formats - Prefer immutable structures: Use
IStructurewhen structure shouldn't change - Check symmetry: Use
SpacegroupAnalyzerto reduce to primitive cell - Validate structures: Check for overlapping atoms or unreasonable bond lengths
File I/O
- Use convenience methods:
from_file()andto()are preferred - Specify formats explicitly: When automatic detection fails
- Handle exceptions: Wrap file I/O in try-except blocks
- Use serialization:
as_dict()/from_dict()for version-safe storage
Materials Project API
- Use context manager: Always use
with MPRester() as mpr: - Batch queries: Request multiple items at once
- Cache results: Save frequently used data locally
- Filter effectively: Use property filters to reduce data transfer
Computational Workflows
- Use input sets: Prefer
MPRelaxSet,MPStaticSetover manual INCAR - Check convergence: Always verify calculations converged
- Track transformations: Use
TransformedStructurefor provenance - Organize calculations: Use clear directory structures
Performance
- Reduce symmetry: Use primitive cells when possible
- Limit neighbor searches: Specify reasonable cutoff radii
- Use appropriate methods: Different analysis tools have different speed/accuracy tradeoffs
- Parallelize when possible: Many operations can be parallelized
Units and Conventions
Pymatgen uses atomic units throughout:
- Lengths: Angstroms (Å)
- Energies: Electronvolts (eV)
- Angles: Degrees (°)
- Magnetic moments: Bohr magnetons (μB)
- Time: Femtoseconds (fs)
Convert units using pymatgen.core.units when needed.
Integration with Other Tools
Pymatgen integrates seamlessly with:
- ASE (Atomic Simulation Environment)
- Phonopy (phonon calculations)
- BoltzTraP (transport properties)
- Atomate/Fireworks (workflow management)
- AiiDA (provenance tracking)
- Zeo++ (pore analysis)
- OpenBabel (molecule conversion)
Troubleshooting
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)
Additional Resources
- Documentation: https://pymatgen.org/
- Materials Project: https://materialsproject.org/
- GitHub: https://github.com/materialsproject/pymatgen
- Forum: https://matsci.org/
- Example notebooks: https://matgenb.materialsvirtuallab.org/
Version Notes
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:
- Python 3.10 or higher
- pymatgen >= 2023.x
- mp-api (for Materials Project access)
How to use pymatgen on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add pymatgen
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches pymatgen from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate pymatgen. Access the skill through slash commands (e.g., /pymatgen) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
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.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★39 reviews- ★★★★★Camila Menon· Dec 20, 2024
pymatgen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chinedu Rao· Dec 16, 2024
I recommend pymatgen for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arjun Reddy· Dec 12, 2024
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Sakshi Patil· Nov 27, 2024
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Camila Bansal· Nov 11, 2024
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Perez· Nov 7, 2024
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
- ★★★★★Arya Reddy· Nov 3, 2024
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zara Liu· Oct 26, 2024
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arjun Martinez· Oct 22, 2024
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Oct 18, 2024
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
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