ic-lattice 0.1.0

Monte Carlo lattice simulation for A-B molecule interactions

ic_lattice

Monte Carlo lattice simulation for A-B molecule interactions on a 2D lattice.

Description

ic_lattice is a Python package that simulates the behavior of two types of molecules (A and B) on a 2D square lattice using Monte Carlo methods. The simulation uses the Metropolis algorithm to sample configurations according to their Boltzmann probabilities.

Features

• Efficient numba-accelerated Monte Carlo simulation
• Periodic boundary conditions
• Configurable interaction energies (A-A, A-B, B-B)
• Real-time monitoring of energy and neighbor statistics
• Both library and command-line interfaces

Installation

Using pixi

pixi add ic_lattice

Or add to your pixi.toml:

[dependencies]
ic_lattice = "*"

Using pip

pip install ic_lattice

From source

git clone https://github.com/yourusername/ic-lattice.git
cd ic-lattice
pip install -e .

Usage

Command-line interface

ic-lattice --N 50 --n_A 1250 --E_AA -1.0 --E_AB 0.0 --E_BB -1.0 --beta 1.0 --steps 10000 --print_interval 1000

Parameters:

• --N: Lattice size (creates N x N grid)
• --n_A: Number of A molecules
• --E_AA: A-A interaction energy
• --E_AB: A-B interaction energy
• --E_BB: B-B interaction energy
• --beta: Inverse temperature (1/kT)
• --steps: Number of Monte Carlo steps
• --print_interval: Print statistics every N steps (0 for no printing)

Python API

from ic_lattice import initialize_lattice, run_simulation

# Initialize lattice
N = 50
n_A = 1250
lattice = initialize_lattice(N, n_A)

# Set parameters
E_AA = -1.0
E_AB = 0.0
E_BB = -1.0
beta = 1.0
n_steps = 10000

# Run simulation
results = run_simulation(
    lattice, N, beta, E_AA, E_AB, E_BB,
    n_steps, print_interval=1000
)

# Access results
print(f"Final energy: {results['final_energy']}")
print(f"Acceptance rate: {results['acceptance_rate']}")
print(f"A-B fraction: {results['final_ab_fraction']}")

Physics

The simulation models a canonical ensemble where:

• The lattice has N×N sites
• Each site contains either an A or B molecule
• Molecules can swap positions via Monte Carlo moves
• Energy depends on nearest-neighbor interactions with periodic boundary conditions

The acceptance probability for a swap follows the Metropolis criterion:

P(accept) = min(1, exp(-β * ΔE))

where β = 1/(kT) is the inverse temperature and ΔE is the energy change.

Development

Setting up development environment with pixi

pixi install
pixi run pytest

Running tests

pytest tests/

Code formatting

black src/
ruff check src/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this package in your research, please cite:

@software{ic_lattice,
  author = {Your Name},
  title = {ic_lattice: Monte Carlo lattice simulation},
  year = {2024},
  url = {https://github.com/yourusername/ic-lattice}
}