A simple, fast, and powerful particle simulator for aerosol science.

Documentation | Examples | PyPI

pip install particula

or via conda:

conda install -c conda-forge particula

import particula as par

# Build an aerosol system
aerosol = (
    par.AerosolBuilder()
    .set_atmosphere(atmosphere)
    .set_particles(particles)
    .build()
)

# Run dynamics (chainable with | operator)
process = par.dynamics.Condensation(strategy) | par.dynamics.Coagulation(strategy)
aerosol = process.execute(aerosol, time_step=10, sub_steps=1000)

particula/
├── gas/           # Gas phase: species, vapor pressure, atmosphere
├── particles/     # Particle representations & distributions
├── dynamics/      # Time-dependent processes
│   ├── coagulation/
│   ├── condensation/
│   └── wall_loss/
├── activity/      # Activity coefficients, phase separation
├── equilibria/    # Gas-particle partitioning
└── util/          # Constants, validation, unit conversion

• Aerosol — Building and inspecting aerosol objects
• Dynamics — Coagulation, condensation, wall loss simulations
• Equilibria — Gas-particle partitioning calculations
• Gas Phase — Vapor pressure, species properties
• Particle Phase — Size distributions, optical properties
• Simulations — Full end-to-end scientific scenarios

• Aerosol Tutorial — Learn how to build gas species, atmospheres, particle distributions, and combine them into an Aerosol object.

• Organic Partitioning & Coagulation — Full simulation of secondary organic aerosol (SOA) formation from 10 organic vapors, followed by Brownian coagulation over 10 minutes.

• Gas & Particle Phases — Full thermodynamic modeling with swappable strategies
• Dynamics — Coagulation, condensation, wall loss, dilution
• Flexible Representations — Discrete bins, continuous PDF, particle-resolved
• Builder Pattern — Clean, validated object construction with unit conversion
• Composable Processes — Chain runnables with | operator

If you use Particula in your research, please cite:

Particula [Computer software]. DOI: 10.5281/zenodo.6634653

MIT