# Atooms tutorial

## Basics

Atooms provides a high-level interface to the main objects of particle-based simulations. It mostly focuses on classical molecular dynamics and Monte Carlo simulations, but it is not limited to that. It can be used to simulate and analyze lattice models such as TASEP or kinetically constrained models.

We will start by having a look at the basic objects of particle-based simulations and how to store them on a file.

### Particles' properties

Particles' positions are stored as numpy arrays, but we can pass a simple list with x, y, z coordinates when we create them

from atooms.system.particle import Particle
particle = Particle(position=[1.0, 0.0, 0.0])
print(particle.position, type(particle.position))

[ 1.  0.  0.] <class 'numpy.ndarray'>



Particles can live in an arbitrary number of spatial dimensions

particle = Particle(position=[1.0, 0.0, 0.0, 0.0, 0.0])
print(len(particle.position))

5



By default, particles have a few more properties such as velocity, chemical species, mass and radius. They can all be altered at will or even set to None.

import numpy
particle = Particle(position=[1.0, 0.0, 0.0], velocity=[1.0, 0.0, 0.0])
particle.species = 'Na'
particle.position += numpy.array([0.0, 1.0, 1.0])
particle.velocity *= 2
print(particle)

Particle(species=Na, mass=1.0, position=[ 1.  1.  1.], velocity=[ 2.  0.  0.], radius=None)



You may want to add physical properties to particles, like charge or whatever. Of course, in python you can do it very easily

particle.charge = -1.0


This won't break anything!

### Dealing with velocities

You may not need velocities at all (for instance because you are working with Monte Carlo simulations) but if you do, atooms provides a few useful methods and functions. For instance, you can assign velocity from a Maxwell-Boltzmann distribution at a temperature T.

particle = [Particle() for i in range(1000)]
for p in particle:
p.maxwellian(T=1.0)
ekin = sum([p.kinetic_energy for p in particle])
ndim = 3
ndof = len(particle) * ndim
T = 2.0 / ndof * ekin
print(T)

0.944755026085



Doing so will leave a non-zero total momentum, but we can fix it (note that all masses are equal)

from atooms.system.particle import fix_total_momentum, cm_velocity
print(cm_velocity(particle))
fix_total_momentum(particle)
print(cm_velocity(particle))

[-0.03078045  0.05653126  0.01857607]
[  1.31006317e-17   7.77156117e-18  -2.07056594e-17]



### Boundary conditions

To avoid major finite size effects, we enclose particles in a cell with periodic boundary conditions. By convention, the cell origin is at the origin of the reference frame.

from atooms.system.cell import Cell
L = 2.0
cell = Cell(side=[L, L, L])
print(cell.side, cell.volume)

[ 2.  2.  2.] 8.0



Atooms provides means to fold particles back in the "central" simulation cell, i.e. the one centered at the origin at the reference frame. For simplicity, let us work with particles in 1d.

cell = Cell(side=1.0)
particle = Particle(position=2.0)  # particle outside the central cell
particle.fold(cell)
print(particle.position)

0.0



The particle is now folded back at the origin.

A related method returns the nearest periodic image of a given particle with respect to another particle

particle_1 = Particle(position=-0.45)
particle_2 = Particle(position=+0.45)
image = particle_1.nearest_image(particle_2, cell, copy=True)
print(image)

Particle(species=A, mass=1.0, position=0.55, velocity=[ 0.  0.  0.], radius=0.5)



### The system object

Objects like particles and the simulation cell can be gathered in an instance of a god-like class called System. The system contains all the relevant physical objects of your simulation. Reservoirs like thermostats, barostats and particle reservoirs can be added as well. These objects are placeholders for thermodynamic state variables like temperature, pressure or chemical potential. Any class meant to describe the interaction between particles also belongs to the system.

Let us build a system with a few particles in a cell and use the system methods to modify the system density and temperature. Note that density and temperature are python properties and thus modify the attributes of particles and cell under the hoods using the set_density and set_temperature methods respectively

from atooms.system import System
system = System(particle=[Particle() for i in range(100)],
cell=Cell([10.0, 10.0, 10.0]))
system.density = 1.2  # equivalent to system.set_density(1.2)
system.temperature = 1.5  # equivalent to system.set_temperature(1.2)
print(system.density, system.temperature)

1.2 1.5



Note that the system temperature is the kinetic one and need not coincide with the one of the thermostat.

from atooms.system import Thermostat
system.thermostat = Thermostat(temperature=1.0)
system.temperature = 1.5  # equivalent to system.set_temperature(1.2)
print(system.temperature, system.thermostat.temperature)

1.5 1.0



### Interaction and backends

Classical particles interact with each other via a potential $$u(\{r_i\})$$, where $$\{r_i\}$$ is the set of particles' coordinates. Atooms relies on third-party efficient backends written in C, Fortran or CUDA to actually compute the interaction between the particles. Here we will use the LAMMPS backend, see Molecular dynamics with LAMMPS for further details. It accepts a string variable that defines the interaction potential using the LAMMPS syntax, see https://lammps.sandia.gov/doc/pair_style.html, and stores a reference to the system object of which we want to compute the energy.

As proof of principle, we compute the interaction energy between two Lennard-Jones particles

from atooms.system import System, Particle, Cell
from atooms.backends.lammps import LAMMPS

x = 1.122  # Minimum of the potential
system = System(particle=[Particle(position=[0.0, 0.0, 0.0]),
Particle(position=[x, 0.0, 0.0])],
cell=Cell([10.0, 10.0, 10.0]))
cmd = """
pair_style      lj/cut 2.5
pair_coeff      1 1 1.0 1.0  2.5
"""
# The backend will add an interaction to the system
backend = LAMMPS(system, cmd)

# Compute and get the potential energy
# The cache option allows to get the potential energy without recalculating it
print(system.potential_energy(), system.potential_energy(cache=True))

-0.99999388 -0.99999388



The energy and forces are stored in system.interaction.energy and system.interaction.forces.

### Trajectory files

To write the state of the system to a file, we use a Trajectory class. Trajectories are composed of multiple frames, each one holding the state of the system at a given step during the simulation. We use a basic xyz format to write the state of the system and then parse the trajectory file we produced to see how it looks like.

from atooms.trajectory import TrajectoryXYZ

system = System(particle=[Particle() for i in range(4)],
cell=Cell([10.0, 10.0, 10.0]))

# Open the trajectory in write mode and write the state of the system
# at step 0
with TrajectoryXYZ('test.xyz', 'w') as th:
th.write(system, step=0)

# Read the xyz file back as plain text
with open('test.xyz') as fh:

4
step:0 columns:id,pos dt:1 cell:10.0,10.0,10.0
A 0.000000 0.000000 0.000000
A 0.000000 0.000000 0.000000
A 0.000000 0.000000 0.000000
A 0.000000 0.000000 0.000000



Note that trajectories are file-like objects: they must be opened and closed, preferably using the with syntax.

Of course, we can write multiple frames by calling write() repeatedly.

with TrajectoryXYZ('test.xyz', 'w') as th:
for i in range(3):
th.write(system, step=i*10)


To get the system back we read the trajectory. Trajectories support iteration and indexing, just like lists.

with TrajectoryXYZ('test.xyz') as th:
# First frame
system = th[0]
print(system.particle[0].position, system.cell.side)

# Last frame
system = th[-1]
print(system.particle[0].position, system.cell.side)

# Iterate over all frames
for i, system in enumerate(th):
print(th.steps[i], system.particle[0].position)

[ 0.  0.  0.] [ 10.  10.  10.]
[ 0.  0.  0.] [ 10.  10.  10.]
0 [ 0.  0.  0.]
10 [ 0.  0.  0.]
20 [ 0.  0.  0.]



### Particles on a lattice

Suppose we want to simulate a system where particles can only be located at discrete sites, say a one-dimensional lattice or perhaps a network with a complex topology. Particle positions can then be described as plain integers, holding the index of the site on which a particle is located. We create such a system and then write it to a file in xyz format

import numpy
from atooms.system import System, Particle

# Build model system with integer coordinates
particle = [Particle() for i in range(3)]
particle[0].position = 0
particle[1].position = 1
particle[2].position = 2
system = System(particle=particle)

# Write xyz trajectory
from atooms.trajectory import TrajectoryXYZ
with TrajectoryXYZ('test.xyz', 'w') as th:
th.write(system, 0)

# Read the xyz file back as plain text
with open('test.xyz') as fh:

3
step:0 columns:id,pos dt:1
A 0
A 1
A 2



Everything went fine. However, we have to tweak things a bit when reading the particles back, to avoid positions being transformed to arrays of floats instead of integers. This can be done with the help of a callback that transforms the system accordingly as we read the trajectory.

# Read file as an xyz trajectory
with TrajectoryXYZ('test.xyz') as th:

# Otherwise they are read as numpy arrays of floats.
def modify(system):
for p in system.particle:
p.position = int(p.position[0])
p.velocity = None
return system

for p in th[0].particle:
print(p)

Particle(species=A, mass=1.0, position=0, velocity=None, radius=None)



Our particles have now integer coordinates. Note that, on passing, we have set to None velocities and radii as they are not relevant in this case.

## Simulations

Within atooms, a simulation is a high-level class that encapsulates some common tasks and provides a consistent interface to the user, while backends are classes that actually make the system evolve. Here, we implement a minimal backend to run a simulation.

At a very minimum, a backend is a class that provides

• a system instance variable, which should (mostly) behave like atooms.system.System.
• a run() method, which evolves the system for a prescribed number of steps (passed as argument)

Optionally, the backend may hold a reference to a trajectory class, which can be used to checkpoint the simulation or to write configurations to a file. This is however not required in a first stage.

### A minimal simulation backend

We set up a bare-bones simulation backend building on the native System class

from atooms.system import System

class BareBonesBackend(object):

def __init__(self):
self.system = System()

def run(self, steps):
for i in range(steps):
pass

# The backend is created and wrapped by a simulation object.
# Here we first call the run() method then run_until()
from atooms.simulation import Simulation
backend = BareBonesBackend()
simulation = Simulation(backend)
simulation.run(10)
simulation.run_until(30)
assert simulation.current_step == 30

# This time we call run() multiple times
simulation = Simulation(backend)
simulation.run(10)
simulation.run(20)
assert simulation.current_step == 30

# Increase verbosity to see a meaningful log
from atooms.core.utils import setup_logging
setup_logging(level=20)
simulation = Simulation(backend)
simulation.run(10)

#
# atooms simulation via <__main__.BareBonesBackend object at 0x7ff54d0527f0>
#
# version: 1.9.1+1.5.0-132-gfe9bc7-dirty (2019-04-12)
# atooms version: 1.9.1+1.5.0-132-gfe9bc7-dirty (2019-04-12)
# simulation started on: 2019-05-17 at 17:36
# output path: None
# backend: <__main__.BareBonesBackend object at 0x7ff54d0527f0>
#
# target target_steps: 10
#
#
# starting at step: 0
#
# simulation ended successfully: reached target steps 10
#
# final steps: 10
# final rmsd: 0.00
# wall time [s]: 0.00
# average TSP [s/step/particle]: nan
# simulation ended on: 2019-05-17 at 17:36


### Simple random walk

We implement a simple random walk in 3d. This requires adding code to the backend run() method to actually move the particles around.

We start by building an empty system. Then we add a few particles and place them at random in a cube. Finally, we write a backend that displaces each particle randomly over a cube of prescribed side.

import numpy
from atooms.system import System

# There are no particles at the beginning
system = System()
assert len(system.particle) == 0

from atooms.system.particle import Particle
from random import random
L = 10
for i in range(1000):
p = Particle(position=[L * random(), L * random(), L * random()])
system.particle.append(p)

class RandomWalk(object):

def __init__(self, system, delta=1.0):
self.system = system
self.delta = delta

def run(self, steps):
for i in range(steps):
for p in self.system.particle:
dr = numpy.array([random()-0.5, random()-0.5, random()-0.5])
dr *= self.delta
p.position += dr


The Simulation class provides a callback mechanism to allow execution of arbitrary code during the simulation. This can be used to write logs or particle configurations to file, or to perform on-the-fly calculations of the system properties. Callbacks are plain function that accept the simulation object as first argument. They are called at prescribed intervals during the simulation.

Here we measure the mean square displacement (MSD) of the particles to make sure that the system displays a regular diffusive behavior $$MSD \sim t$$

from atooms.simulation import Simulation
simulation = Simulation(RandomWalk(system))

# We add a callback that computes the MSD every 10 steps
# We store the result in a dictionary passed to the callback
msd_db = {}
def cbk(sim, initial_position, db):
msd = 0.0
for i, p in enumerate(sim.system.particle):
dr = p.position - initial_position[i]
msd += numpy.sum(dr**2)
msd /= len(sim.system.particle)
db[sim.current_step] = msd

# We will execute the callback every 10 steps
simulation.add(cbk, 10, initial_position=[p.position.copy() for p in
system.particle], db=msd_db)
simulation.run(50)

# The MSD should increase linearly with time
time = sorted(msd_db.keys())
msd = [msd_db[t] for t in time]

print(time, msd)
import matplotlib.pyplot as plt
plt.cla()
plt.plot(time, msd, '-o')
plt.xlabel("t")
plt.ylabel("MSD")
plt.savefig('msd.png')

[0, 10, 20, 30, 40, 50] [0.0, 2.4646764004435413, 4.9302643884563624, 7.5967157934411889, 10.195445884554854, 12.668286408937636]
/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')



The MSD as a function of time should look linear.

### Molecular dynamics with LAMMPS

Atooms provides a simulation backend for LAMMPS, an efficient and feature-rich molecular dynamics simulation package. The backend accepts a string variable containing regular LAMMPS commands and initial configuration to start the simulation. The latter can be provided in any of the following forms:

• a System object
• a Trajectory object
• the path to an xyz trajectory

In the last two cases, the last configuration will be used to start the simulation.

Here we we use the first configuration of an existing trajectory for a Lennard-Jones fluid

import atooms.trajectory as trj
from atooms.backends.lammps import LAMMPS

import os
system = trj.TrajectoryXYZ('../../data/lj_N1000_rho1.0.xyz')[0]
cmd = """
pair_style      lj/cut 2.5
pair_coeff      1 1 1.0 1.0  2.5
neighbor        0.3 bin
neigh_modify    check yes
timestep        0.002
"""
backend = LAMMPS(inp, system)


We now wrap the backend in a simulation instance. This way we can rely on atooms to write thermodynamic data and configurations to disk during the simulation: we just add the write_config() and write_thermo() functions as observers to the simulations. You can add your own functions as observers to perform arbitrary manipulations on the system during the simulation. Keep in mind that calling these functions causes some overhead, so avoid calling them at too short intervals.

from atooms.simulation import Simulation
from atooms.system import Thermostat
from atooms.simulation.observers import write_thermo, write_config

# We create the simulation instance and set the output path
sim = Simulation(backend, output_path='lammps.xyz')
# Just store a reference to the trajectory class you want to use
sim.trajectory = trj.TrajectoryXYZ
# Write configurations every 500 steps in xyz format
# Write thermodynamic properties every 500 steps


We add a thermostat to keep the system temperature at T=2.0 and run the simulations for 10000 steps.

backend.system.thermostat = Thermostat(temperature=2.0, relaxation_time=0.1)
sim.run(10000)


Note that we use atooms Thermostat object here: the backend will take care of adding appropriate commands to the LAMMPS script.

We have a quick look at the kinetic temperature as function of time to make sure the thermostat is working

We can use the postprocessing package to compute the radial distribution function

from atooms.postprocessing import api
api.gr('lammps.xyz')


### Energy minimization with LAMMPS

It is possible to minimize the energy of a system to determine its so-called inherent structure using LAMMPS as a backend. To achieve this, atooms defines an Optimization class, which behaves mostly as Simulation except that it stops when the mean square total force $W=\frac{1}{N}\sum_i |f_i|^2$ is lower than a given tolerance.

from atooms.trajectory import TrajectoryXYZ
from atooms.optimization import Optimization
from atooms.backends.lammps import EnergyMinimization
cmd = """
pair_style      lj/cut 2.5
pair_modify     shift yes
pair_coeff      1 1 1.0 1.0 2.5
"""
system = TrajectoryXYZ('../../data/lj_N256_rho1.0.xyz')[0]
bck = EnergyMinimization(system, cmd)
opt = Optimization(bck, tolerance=1e-10)
opt.run()


We check that $$W$$ is lower than the requested tolerance

e_final = system.potential_energy(per_particle=True)
w_final = system.force_norm_square(per_particle=True)
print('Energy={}, mean square force={:.2g}'.format(e_final, w_final))

Energy=-6.8030584, mean square force=3.6e-11



## Trajectories

### Flexible trajectory output

We can customize the format of trajectory files using the fields variable. It contains a list of the particle properties to be written to the trajectory. For this simple example we use again the xyz trajectory format.

We add a charge property to each particle and then instruct the trajectory to write it along with the position

from atooms.system import System, Cell, Particle
system = System(particle=[Particle() for i in range(3)],
cell=Cell([10.0, 10.0, 10.0]))

for p in system.particle:
p.charge = -1.0

with TrajectoryXYZ('test.xyz', 'w', fields=['position', 'charge']) as th:
th.write(system, step=0)

with open('test.xyz') as fh:

3
step:0 columns:position,charge dt:1 cell:10.0,10.0,10.0
0.000000 0.000000 0.000000 -1.0
0.000000 0.000000 0.000000 -1.0
0.000000 0.000000 0.000000 -1.0



The fields list can contain any particle property, even those defined dynamically at run time, such as the charge variable above which is not a predefined particle property!. When reading back the trajectory, the charge property is automatically recognized and added to the particle.

with TrajectoryXYZ('test.xyz') as th:
system = th[0]
print(system.particle[0].charge)

-1.0



### Conversion between trajectory formats

Atooms provides means to convert between trajectory various formats. At a very basic level, this requires opening the original trajectory for reading and the new one for writing using the desired trajectory class. Here we convert an xyz trajectory in a format suitable for the LAMMPS package

from atooms.trajectory import TrajectoryLAMMPS
with TrajectoryXYZ('test.xyz') as th_inp,\
TrajectoryLAMMPS('test.lammps', 'w') as th_out:
for i, system in enumerate(th_inp):
th_out.write(system, th_inp.steps[i])


The convert() function wraps the conversion in a more convenient interface

from atooms.trajectory import convert
convert(TrajectoryXYZ('test.xyz'), TrajectoryLAMMPS, 'test.lammps')


There are several optional parameters that allows to customize the trajectory output, see the function signature for more details.

Finally, the trj.py script installed by atooms allows to quickly convert trajectories on the command-line, which is actually the most frequent use case

trj.py convert -i xyz -o lammps test.xyz test.lammps


Although the script will do its best to guess the appropriate trajectory formats, it is best to provide the input and output trajectory formats via the -i and -o flags explicitly.