mat-grand-canonical-mc

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.

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.

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.

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.

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.