mat-phase-diagram
ResearchRetrieve and visualize pre-computed phase diagrams from Materials Project for thermodynamic stability analysis.
How to use this skill
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Phase Diagram Retrieval
Goal
Retrieve pre-computed phase diagrams from Materials Project to analyze thermodynamic stability, competing phases, and convex hull relationships. Phase diagrams are essential for understanding:
- Which compounds are stable at 0 K
- Energy above hull (formation energy distance to the convex hull)
- Competing phases that may form during synthesis
- Thermodynamic driving forces for phase transformations
Instructions
1. Basic Phase Diagram Retrieval
Retrieve a phase diagram for a chemical system:
# Env: base-agent
python .agents/skills/mat-phase-diagram/scripts/get_phase_diagram.py \
--chemsys "Li-O" \
--output li_o_phase_diagram.json
Output: JSON file containing the phase diagram data (entries, energies, hull)
2. Generate Phase Diagram Plot
Add --plot flag to create a visualization:
# Env: base-agent
python .agents/skills/mat-phase-diagram/scripts/get_phase_diagram.py \
--chemsys "Li-O" \
--output li_o_pd.json \
--plot li_o_pd.png
Output: PNG plot showing the convex hull and stable/unstable phases
3. Use Different DFT Functional
By default, retrieves GGA+U phase diagrams. For R2SCAN data:
# Env: base-agent
python .agents/skills/mat-phase-diagram/scripts/get_phase_diagram.py \
--chemsys "Li-Fe-P-O" \
--thermo_type "R2SCAN" \
--output lifepo4_r2scan_pd.json \
--plot lifepo4_r2scan_pd.png
Thermo types:
GGA_GGA+U(default): Standard PBE with U correctionsR2SCAN: Meta-GGA functional (more accurate)GGA: Pure PBE without U corrections
Integration with Other Skills
With material-stability Skill
Use phase diagrams to visualize competing phases:
# 1. Get phase diagram
python .agents/skills/mat-phase-diagram/scripts/get_phase_diagram.py \
--chemsys "Li-O" \
--output li_o_pd.json \
--plot li_o_pd.png
# 2. Check specific material's position on hull
python .agents/skills/mat-db-mp/scripts/query_mp.py \
--formula "Li2O" \
--properties energy_above_hull formation_energy_per_atom \
--output li2o_stability.json
With MatterGen Fine-Tuning
Query stable structures from a chemical system for training data:
# 1. Visualize phase diagram to understand stable phases
python .agents/skills/mat-phase-diagram/scripts/get_phase_diagram.py \
--chemsys "Li-S" \
--output li_s_pd.json \
--plot li_s_pd.png
# 2. Query all stable structures
python .agents/skills/mat-db-mp/scripts/query_mp.py \
--chemsys "Li-S" \
--e_above_hull_max 0.0 \
--properties energy_above_hull formation_energy_per_atom \
--output li_s_stable.json
Constraints
- API Key Required: Set
MP_API_KEYenvironment variable - Pre-Computed Only: Returns Materials Project's pre-computed phase diagrams. Cannot compute custom phase diagrams with user-provided energies.
- Chemical System Format: Use hyphen-separated elements (e.g., "Li-O", not "LiO" or "Li O")
- Availability: Not all chemical systems have pre-computed phase diagrams. Common systems (binary oxides, battery materials) are well-covered.
- Thermo Type Availability: R2SCAN phase diagrams are available for a subset of systems
- Environment: Requires
base-agentconda environment - Dependencies:
mp-api,pymatgen,matplotlib(all installed in base-agent)
When to Use This vs. Calculate
Use MP Phase Diagrams (this skill) when:
- You need quick exploratory analysis
- You want DFT-quality phase diagrams without running calculations
- You're analyzing common chemical systems
- You need GGA+U or R2SCAN accuracy
Calculate Custom Phase Diagrams when:
- You have MLIP-relaxed structures not in MP
- You need to compare MLIP formation energies
- Your chemical system is not in MP
- You want to use specific MLIP models or DFT settings
For custom phase diagram construction, see material-stability skill which can build phase diagrams from user-provided energies.
Examples
See examples/ directory for:
- Li-O binary phase diagram retrieval and visualization
- Li-Fe-P-O quaternary phase diagram (LiFePO4 system)
- Integration with stability analysis workflow
Author: Bowen Deng Contact: GitHub @learningmatter-mit