Amorphorization

Goal

To generate disordered, amorphous structures from crystalline inputs using molecular dynamics (MD). This is achieved through a "melt-quench" protocol, where the material is heated above its melting point and then rapidly cooled to "freeze" the liquid-like disorder.

Protocol: Melt-Quench

The standard Computational amorphization protocol involves:

1. Supercell Setup: The system must be large enough to avoid spurious periodicity effects in the amorphous state. Generally, $>100$ atoms is recommended.
2. Melting (Stage A): Heat the system to $T_{melt}$. $T_{melt}$ should be significantly higher than the experimental melting point (often 1000K higher) to ensure rapid loss of crystalline memory within MD timescales.
3. Equilibration (Stage A/B): Maintain the liquid at $T_{melt}$ for several picoseconds to ensure structural randomized.
4. Quenching (Stage B): Cool the system linearly to the target temperature (e.g., 300K).
   • Cooling Rate: A critical parameter. Typical MD cooling rates are $1-10$ K/ps ($10^{12}-10^{13}$ K/s). Slower rates yield more stable, realistic amorphous structures but are computationally expensive.
5. Annealing/Equilibration (Stage C): Relax the density and local structure at the target temperature.
6. Quenched/Static Relaxation (Stage D): Perform a final geometry optimization (0K) to find the local energy minimum of the amorphous state.

Instructions

1. Preparation

• Supercell: Use the prep_supercell.py helper script. By default, it generates an orthorhombic conventional supercell with approximately 100 atoms, ensuring a robust starting point for amorphization.

python .agents/skills/mat-amorphization/scripts/prep_supercell.py --input crystalline.cif --output supercell.cif

• Foundation Potential: Select a robust model like MACE-MP-large or CHGNet using the mcp_mace_load_model (or similar) tool.

2. Execution (The Melt-Quench Cycle)

Amorphization is performed by calling the run_md tool in a sequence:

Stage 1: Melting

Heat the system to a high temperature (e.g., 3000K) to eliminate crystalline order.

• Tool: mcp_mace_run_md
• Thermostat: nvt_langevin (Robust for high-T dynamics).
• Parameters: temperature=3000, steps=5000 (10 ps), ensemble="nvt_langevin", timestep=2.0.

Stage 2: Quenching

Cool the system rapidly to the target temperature (e.g., 300K).

• Tool: mcp_mace_run_md
• Thermostat: nvt_langevin (Supports specific set_temperature ramping).
• Monitor: Use monitor_type="quenching" and monitor_params={"temperature_end": 300, "steps": 5000}.
• Parameters: temperature=3000 (start), steps=5000 (10 ps), ensemble="nvt_langevin".
• Note: Ensure the input structure is the output of Stage 1.

Stage 3: Equilibration

Relax the structure at the target temperature to reach equilibrium distribution.

• Tool: mcp_mace_run_md
• Thermostat: nvt_bussi (Bussi-Donadio-Parrinello) - Provides correct canonical sampling.
• Parameters: temperature=300, steps=2500 (5 ps), ensemble="nvt_bussi".

3. Analysis & Verification

Use the analyze_amorphous.py script to verify the results:

• RDF (Radial Distribution Function): Confirm the absence of long-range order.
• Coordination Number: Check local bonding environments.

Helper Scripts

• prep_supercell.py: Expands a unit cell to a supercell.
• analyze_amorphous.py: Calculates RDF and coordination numbers from the final structure.
• RDF (Radial Distribution Function): Crystalline structures show discrete, sharp peaks at long distances. Amorphous structures show a sharp first peak, a broader second peak, and then decay to 1.0 (no long-range order).
• Coordination Number: Check if the local coordination (e.g., 4 for Si) is maintained despite the global disorder.

Foundation Potential Selection

• ml-foundation-potentials
• MACE-MP-large or CHGNet are recommended for high-temperature MD as they are trained on diverse configurations.

Examples

See .agents/skills/mat-amorphization/examples/ for validated amorphous structures.

Author: Bowen Deng Contact: GitHub @learningmatter-mit