mat-grand-canonical-mc

What problem does it solve?

This Skill generates temperature–composition phase diagrams for alloy systems by running Grand Canonical Monte Carlo (GCMC) simulations driven by cluster expansion chemical potential sweeps.

Core Features & Use Cases

• Run GCMC with chemical-potential control: Sweeps a chosen chemical potential range at fixed temperatures to sample equilibrium compositions in the semigrand canonical ensemble.
• Cluster-expansion backed thermodynamics: Uses a trained cluster expansion model (CE) to compute energies and guide sampling of atomic configurations.
• Produce analysis-ready outputs: Writes summary JSON plus analysis scripts to generate chemical-potential vs composition curves and T–x phase diagrams.

Quick Start

Run the chemical potential sweep with a trained cluster expansion on a 3×3×3 supercell using: python .agents/skills/mat-grand-canonical-mc/scripts/run_gcmc_sweep.py --ce_file cluster_expansion.json --supercell 3 3 3 --temperatures 400 600 800 --mu_min -0.4 --mu_max 0.4 --num_mu_points 20 --steps 50000 --equilibration_steps 10000 --element Ag --output_dir gcmc_results/