mat-grand-canonical-mc

Run grand canonical Monte Carlo simulations to map alloy phase behavior via chemical-potential sweeps.

144|21|Updated Jan 8, 2026

Search Tags:#thermodynamics #phase diagrams #materials simulation #grand canonical monte carlo #cluster expansion #chemical potential sweep #MC analysis

Ready to run

npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-grand-canonical-mc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mat-grand-canonical-mc
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-grand-canonical-mc
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-grand-canonical-mc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires smol, pymatgen, numpy, matplotlib, json, pathlib, and includes scripts (resource) components.

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/

Frequently Asked Questions about mat-grand-canonical-mc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I map alloy phase diagrams with grand canonical Monte Carlo simulations?▼

To generate a T-x phase diagram, run a chemical potential sweep using a trained cluster expansion JSON file, a supercell matrix, and specified temperature ranges to produce per-point composition statistics and visualization plots.

What is a chemical potential sweep in the context of materials simulation?▼

A chemical potential sweep varies chemical potential across a defined range at fixed temperatures to sample equilibrium compositions in the semigrand canonical ensemble, enabling exploration of miscibility gaps and phase behavior in binary alloy thermodynamics.

Do I need a trained cluster expansion model to run GCMC for thermodynamics studies?▼

Yes, a trained cluster expansion model in JSON format is required to compute energies and guide sampling of atomic configurations during the grand canonical Monte Carlo simulations for alloy thermodynamics studies.

Can I use pymatgen and smol for cluster expansion thermodynamics and phase diagram generation?▼

Yes, pymatgen and smol are supported dependencies for running cluster expansion-backed grand canonical Monte Carlo simulations, producing summary JSON files and scripts to generate chemical potential versus composition curves and T-x phase diagrams.

What are the limitations of using GCMC for binary alloy phase diagram mapping?▼

GCMC for binary alloy phase diagram mapping is primarily suited for miscibility-gap exploration and requires a trained cluster expansion JSON, a defined supercell matrix, and adequate equilibration steps to ensure accurate composition and energy statistics.