Overview#
atooms is a high-level Python framework for simulations of interacting particles, such as molecular dynamics or Monte Carlo simulations.
It is composed by a core package, which provides a consistent interface to the basic objects of particle-based simulations, and feature packages built on top of it to implement complex simulation methods and analysis tools.
Feature packages are available from the atooms main repository. They are installed in the
atooms
namespace to prevent name clashing. This allows frontends to evolve independent of the underlying core package.
Design principles#
Focus on a simple and expressive interface
API refined over the years towards consistency
Modular and extensible design via namespace packages
Semantic versioning - for what is worth
Easy to interface: in-house codes and custom formats are first-class citizens
Support for efficient simulation backends, with a focus on GPU codes
Quick start#
Here is a small example for setting up a mixture of two types of particles, A and B, in a periodic elongated cell. The number density is set to unity.
from atooms.system import System
system = System(N=64)
system.replicate(times=4, axis=0)
system.composition = {'A': 128, 'B': 128}
system.density = 1.0
Particles in the central part of the cell get a random displacement and are folded back into the simulation cell
import numpy
for p in system.particle:
if abs(p.position[0]) < system.cell.side[0] / 4:
p.position += 0.5 * (numpy.random.random() - 0.5)
p.fold(system.cell)
system.show('ovito')
Simulation data are stored in trajectory files, which are easy to manipulate and convert with atooms. Here, we write the system species and positions in a single-frame trajectory file using the xyz format.
from atooms.trajectory import TrajectoryXYZ
with TrajectoryXYZ('input.xyz', 'w') as th:
th.variables = ['species', 'position'] # actually, this is the default
th.write(system)
The trajectory file can now be used to start a simulation using one the available simulation backends or your own code.