Introduction

Introduction#

MoleCool is a Python library for numerical modeling of light-matter interactions, with a focus on laser cooling of molecules — from simple few-level systems and diatomics to polyatomics and radioactive species.

MoleCool enables you to

simulate, analyze, and visualize the dynamics of laser-molecule interactions for designing and optimizing experimental setups of cutting-edge cooling and trapping experiments.

Note

For more details, please refer to our paper and cite it when using this code: arXiv paper

Key Features#

  • Flexible dynamics solvers Supports rate equations and Optical Bloch equations (OBEs) to evaluate interactions in the presence of external magnetic fields. MoleCool reads predefined constants such as dipole matrix elements, hyperfine frequencies, and g-factors from JSON files.

  • Interactive level scheme handling Intuitive tools to explore and adjust electronic, vibrational, and rotational level structures. MoleCool can automatically generate clear visualizations of:

    • Intricate level schemes

    • Transition spectra

    • Time-dependent light-matter dynamics

  • Laser cooling force profiles Efficient evaluation of cooling force profiles across high-dimensional parameter spaces, enabling fast optimization for complex cooling configurations.

  • Monte Carlo trajectory simulations Simulate the motion of many individual particles through laser fields using pre-evaluated force profiles for statistically meaningful results.

  • Spectra analysis module An independent spectra module allows analysis and fitting of molecular spectra using an effective Hamiltonian. This makes it possible to extract spectroscopic constants (dipole matrix elements, hyperfine frequencies, g-factors, …), which can then be fed back into the dynamics simulations.

Target Audience#

MoleCool is intended for researchers and physicists in the field of atomic and molecular laser cooling, who require a versatile simulation framework to:

  • Model laser-molecule interactions

  • Optimize experimental configurations

  • Interpret or predict spectroscopic measurements

Quick Example#

Below is a minimal example of setting up a molecular system, adding lasers, switching on a magnetic field, and running both OBE and rate equation simulations:

from MoleCool import System

system = System(load_constants='138BaF')

# Build level scheme
system.levels.add_all_levels(v_max=0)
system.levels.X.del_lossstate()

# Add laser configuration
system.lasers.add_sidebands(
    lamb        = 859.83e-9,
    P           = 20e-3,
    offset_freq = 19e6,
    mod_freq    = 39.33e6,
    sidebands   = [-2, -1, 1, 2],
    ratios      = [0.8, 1, 1, 0.8]
)

# Magnetic field
system.Bfield.turnon(strength=5e-4, direction=[1, 1, 1])

# Dynamics simulations
system.calc_OBEs(t_int=8e-6, dt=1e-9,
                 magn_remixing=True)
system.calc_rateeqs(t_int=8e-6, magn_remixing=True,
                    position_dep=True)
system.plot_N()

For further details, see the Installation and Getting started sections.

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