Diffusion Analysis

Goal

To accurately calculate the ionic diffusivity ($D$) and activation energy ($E_a$) of specific atomic species in a material using Molecular Dynamics (MD) trajectories and the Arrhenius relation: $D(T) = D_0 \exp\left(-\frac{E_a}{k_B T}\right)$.

Instructions

1. MD Preparation: Run NVT or NPT MD simulations at multiple temperatures (typically 4-6 points between 600K and 1200K).

   • Use the run_md tool from a relevant potential skill (e.g., mace or matgl).
   • Batch Processing: You can pass a directory or a list of CIF paths to structure_data to run multiple MD simulations concurrently via the MCP tool.
   • Supercell Expansion: Ensure supercells are sufficiently large (> 10 Å in all dimensions). The run_md tool natively supports this via the supercell_min_length argument (defaults to 10.0 Å) which performs orthogonal expansion automatically.
   • Optimization: Use the diffusion monitor (see mat-md-monitors) to automatically stop simulations once the transport properties have converged.

     mace.run_md(
         structure_data=["candidates/A.cif", "candidates/B.cif"],
         temperature=600,
         supercell_min_length=10.0,
         monitor=True,
         monitor_type="diffusion",
         monitor_params={"specie": "Li", "threshold": 0.05, "check_interval_ps": 5.0}
     )
     

     Note: If the diffusion monitor triggers an early stop, it will automatically save the diffusion_{specie}.json and msd_{specie}.png directly into the trajectory output directory. You can skip Step 2 and proceed directly to Step 3 for any trajectories that converged early.

2. Individual Diffusivity Analysis: For each temperature directory that did not hit the early stopping criteria, run the analysis script to extract the diffusivity and Mean Square Displacement (MSD).

   # Env: base-agent
   python .agents/skills/mat-diffusion-analysis/scripts/analyze_diffusion.py \
       results/md_600K/trajectory.traj \
       --species Li \
       --temperature 600 \
       --ignore_ps 5.0 \
       --output_dir results/md_600K
   

   • --ignore_ps: Time to skip for equilibration. Default is 5.0 ps.
   • The script automatically detects the frame interval from the .log file if present.

3. Activation Energy Fitting: Once all individual results are generated, use the fitting script to combine data and perform a weighted Arrhenius fit.

   # Env: base-agent
   python .agents/skills/mat-diffusion-analysis/scripts/calculate_activation_energy.py results/
   

   • The script looks for md_*K/diffusion_results.json patterns.
   • It performs error propagation to calculate uncertainty in $E_a$ and extrapolated room-temperature conductivity.

Examples

• Superionic Conductor (LGPS): A complete workflow demonstration including supercell preparation, multi-temperature MD, and final Arrhenius plotting for $Li_{10}GeP_2S_{12}$ is available in the LGPS Example.

Constraints

• Trajectory Format: Trajectories MUST be in ASE .traj format.
• Environments: All analysis scripts require the base-agent conda environment.
• Linearity: The diffusivity calculation assumes a linear diffusive regime. Always inspect the generated MSD plots to ensure linearity after the ignore_ps period.
• Atom Count: To ensure statistical significance, the system should contain a sufficient number of mobile ions (> 20 recommended).
• Paths: Always use relative paths from the project root when executing scripts.

See Also

• mat-md-monitors: Real-time monitoring tools for MD simulations.

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Author: Bowen Deng Contact: GitHub @learningmatter-mit