mcpy

Grand Canonical Monte Carlo for atomistic systems — with native Replica Exchange and machine-learning interatomic potentials.

mcpy predicts the composition and stability of surfaces and nanoparticles under realistic temperature and chemical-potential conditions. It is built on the Atomic Simulation Environment (ASE), so any ASE-compatible calculator — DFT, classical potentials, or MLIPs such as MACE — can drive the sampling.

Highlights

• GCMC and Replica-Exchange GCMC in a single, modular run loop.

• Hybrid scheme of Senftle et al. — every trial insertion/deletion is followed by a short local relaxation, which makes acceptance realistic in densely packed metallic systems.

• Calibratable free volume via Monte Carlo sampling with element-wise exclusion radii.

• Cell geometries out of the box — periodic box, rectangular sub-slab, spherical region around a nanoparticle, and user-defined custom cells.

• Modular trial moves — insertion, deletion, displacement, permutation, shake, and Brownian moves, mixed through a weighted MoveSelector.

• MLIP-ready — MACE, NequIP, ACE, and an optional GPU-native NVIDIA Alchemi backend for large systems.

• Phase-diagram utilities for post-processing GCMC ensembles into surface and nanoparticle phase diagrams.

Citing mcpy

If you use mcpy in a publication, please cite the project repository and the hybrid GCMC method it implements (see Bibliography).

Get started

• Installation
• Your first simulation

Background

• Ensembles
• Cells
• Calibrating species_radii
• Moves

Tutorials

• Oxidation phase diagram of a metallic surface

Examples

• Examples

Reference

• Glossary
• Bibliography