Melting Point

Goal

To determine the thermodynamic melting temperature ($T_m$) of a bulk material by equilibrating a solid-liquid interface in an NVE ensemble.

Instructions

1. Background Research:

   • Search for the approximate melting point ($T_m$) and boiling/evaporation point ($T_{vap}$) of the material.
   • Choose a melting temperature $T_{melt}$ where $T_m < T_{melt} \ll T_{vap}$.
   • MD Parameters: Refer to the mat-md-monitors skill for best practices on timesteps and monitors. In general, use a 2.0 fs timestep for systems without Hydrogen.

2. Phase Preparation:

   • Solid: Create a supercell using create_supercell.py.

   # Env: base-agent
   python .agents/skills/mat-melting-point/scripts/create_supercell.py [input_structure.cif] [solid_supercell.cif] --min_length 20.0
   

   • Liquid: Melt a block using 1D-NPT (with mask) to ensure matching dimensions.

   mcp_mace_run_md(
       structure_data="solid_supercell.cif",
       temperature=2000,  # REPLACE with T_melt from Step 1
       ensemble="npt",
       steps=5000,
       timestep=2.0,      # Use 2.0 fs for most inorganic systems
       pressure=1.0,      # Apply positive pressure (1-2 bar) to prevent evaporation
       pressure_mask=[1, 0, 0], # REQUIRED: Must match the stacking axis (e.g., x-axis)
       output_dir="melt_stage"
   )
   

   • Visual Inspection (CRITICAL): Sometimes the cell does not fully melt within the specified MD steps. You MUST use the mcp_base_visualize_structure tool to generate an image of the final liquid.cif structure (or trajectory) and have the VLM visually inspect the image to confirm that the long-range crystalline order has been destroyed and the cell is completely melted. If it has not, you must run the MD with a higher temperature or for more steps.

3. Interface Creation: Use create_interface.py to concatenate the two phases.

   # Env: base-agent
   python .agents/skills/mat-melting-point/scripts/create_interface.py solid.cif liquid.cif --axis 0 --output interface.cif
   

4. Relaxation: Perform an ionic relaxation using the relax_structure MCP tool with relax_cell=True. This allows the unit cell to adjust (shrink/expand) to match the density, and remove the interface energy created by stacking the two cells.

   mcp_mace_relax_structure(structure_data="interface.cif", relax_cell=True)
   

5. Phase Verification: Before running production MD, verify solid-liquid coexistence in all structures.

   First, extract reference atomic features:

   # Env: mace-agent (or matgl-agent)
   # Extract from pure solid - use explicit output path
   mcp_mace_predict_atomic_features(
       structure_data="solid_supercell.cif",
       output_path="<research_dir>/solid_features.json"
   )
   
   # Extract from pure liquid - use explicit output path
   mcp_mace_predict_atomic_features(
       structure_data="liquid_supercell.cif",
       output_path="<research_dir>/liquid_features.json"
   )
   

   Then verify phases:

   # Env: base-agent
   # Solid should be ~100% solid
   python .agents/skills/mat-melting-point/scripts/check_phase.py <research_dir>/solid_features.json \
       --solid_features <research_dir>/solid_features.json \
       --liquid_features <research_dir>/liquid_features.json
   
   # Liquid should be ~100% liquid
   python .agents/skills/mat-melting-point/scripts/check_phase.py <research_dir>/liquid_features.json \
       --solid_features <research_dir>/solid_features.json \
       --liquid_features <research_dir>/liquid_features.json
   
   # Interface should show ~50% solid/liquid coexistence
   # (Requires predicting features for the relaxed interface first)
   mcp_mace_predict_atomic_features(
       structure_data="interface_relax/relaxed_structure.cif",
       output_path="<research_dir>/interface_features.json"
   )
   python .agents/skills/mat-melting-point/scripts/check_phase.py <research_dir>/interface_features.json \
       --solid_features <research_dir>/solid_features.json \
       --liquid_features <research_dir>/liquid_features.json
   

   Expected:

   • Solid: ≥95% solid
   • Liquid: ≥95% liquid
   • Interface: 40-60% solid (coexistence maintained)

   If interface lost coexistence: Adjust melting temperature or relaxation parameters.

6. Thermalization (Equilibration): Start from the 0 K relaxed structure and run a short NVT thermalization at the expected $T_m$ to properly distribute kinetic and potential energy.

   mcp_mace_run_md(
       structure_data="interface_relax/relaxed_structure.cif",
       temperature=933, # Target expected Tm
       ensemble="nvt",
       steps=5000,
       timestep=2.0,
       output_dir="thermalization_md"
   )
   

7. Production: Run an NVE MD simulation starting from the full .traj file of the thermalized structure with the monitor=True parameter. Passing the .traj file is critical because it preserves the velocities from the NVT run, providing a continuous MD sequence. bash mcp_mace_run_md( structure_data="thermalization_md/<formula>_<temp>K_nvt.traj", # Pass .traj to preserve velocities temperature=933, ensemble="nve", steps=100000, timestep=2.0, monitor=True, monitor_type="melting", output_dir="production_md" )

8. Auto-Termination: The integrated monitor will:

   • Check for temperature and potential energy stability.
   • Automatically stop the MD simulation when the melting point is reached.
   • Log termination status in the research log.
   • mcp_mace_run_md will return once the simulation stops (either by finishing all steps or by monitor termination).

9. Phase Validation: Verify that the solid and liquid phases still coexist at the end of the simulation. First, predict the atomic features of the final structure:

   mcp_mace_predict_atomic_features(
       structure_data="production_md/final_structure.cif",
       output_path="production_md/final_structure_features.json"
   )
   

   Then, classify the phase:

   # Env: base-agent
   python .agents/skills/mat-melting-point/scripts/check_phase.py production_md/final_structure_features.json \
     --solid_features solid_features.json \
     --liquid_features liquid_features.json
   

   • Fully Solidified: The NVE starting temperature was too low.
   • Fully Melted: The NVE starting temperature was too high.

10. Analysis: If coexistence is verified, calculate $T_m$ by averaging the temperature over the last 5 ps of the simulation.

Examples

Creating a solid-liquid interface for Aluminum:

# Env: base-agent
python .agents/skills/mat-melting-point/scripts/create_interface.py Al_solid.cif Al_liquid.cif --axis 0 --output Al_interface.cif

Constraints

• Box Dimensions: The lattice parameters perpendicular to the stacking axis must be identical for both solid and liquid blocks.
• Ensemble: The final production run must be in the NVE ensemble to allow the temperature to evolve to $T_m$.
• Environments: Different MLIPs require specific Conda environments (e.g., mace-agent, matgl-agent). Ensure the scripts are run within the correct environment for the chosen model.

---

Author: Bowen Deng Contact: GitHub @learningmatter-mit