Molecular dynamics examples

In this example, we will perform a MD simulation of a Lennard-Jones fluid. The purpose of this example is to illustrate two different ways we can use PyRETIS:

The simulation we set up is rather ordinary and we will start from a regular lattice and simulate the melting at a specified temperature as illustrated in the figure below.

NVE simulation of a LJ fluid

Fig. 15 Initial and final structure of the Lennard-Jones simulation. The initial structure (left) is a FCC lattice, while the final structure is less ordered.

Molecular dynamics using an input file

We can execute PyRETIS by creating an input file and using the PyRETIS application. In order to run this example, merge the code-snippets given below into one file, say md.rst, which then can be executed by:

pyretisrun -i md.rst

In the first part of the input file, we will specify the type of simulation to run and the number of steps to perform by defining the simulation section:

Molecular dynamics example settings

Simulation settings
task = md-nve
steps = 1000

In order to produce some actual dynamics, we will have to integrate the equations of motion. The engine to use for this is selected using the engine section:

Engine settings
class = velocityverlet
timestep = 0.002

and we define the system we are studying using the system section and the particles section: and the particles:

System settings
temperature = 2.0
units = lj

position = {'generate': 'fcc',
            'repeat': [3, 3, 3],
            'density': 0.9}

velocity = {'generate': 'maxwell',
            'temperature': 2.0,
            'momentum': True,
            'seed': 0}

mass = {'Ar': 1.0}
name = ['Ar']
type = [0]

We also need to specify a force field, which is done by specifying the forcefield section and one or more potential sections. In this case, the force field is set up using a single type of potential function:

Forcefield settings
description = Lennard Jones test

class = PairLennardJonesCutnp
shift = True
parameter 0 = {'sigma': 1.0, 'epsilon': 1.0, 'rcut': 2.5}

and we modify the output created by specifying the output section:

backup = overwrite
energy-file = 1
order-file = 10
cross-file = 1
trajectory-file = 10

What we are essentially modifying here is the frequency of output, and we are also telling PyRETIS not to back-up, but to overwrite old output files that might be present in the same directory (this is controlled by the backup = False keyword setting).

After running this example, the following files should have been created:

  • energy.txt
    This file contains energies and temperature as a function of time/step in the MD simulation:
  • thermo.txt
    This file contains the same energies as energy.txt and in addition the pressure.
    This file contains the trajectory and can be visualised using for instance VMD.
  • pyretis.log
    This file contains output messages from the PyRETIS application.
  • out.rst
    This file contains the input settings you provided as PyRETIS read them. This can be used to check the correctness of the input file.

Plotting the pressure from thermo.txt should then give a figure similar to:

Pressure, obtained from the MD NVE simulation

Fig. 16 Representative output from the MD NVE simulation: The eneriges, pressure and the temperature as a function of the simulation step.

Molecular dynamics using the PyRETIS library

In this part of the example, we will make explicit use of the PyRETIS library If you want to try this example, you will have to copy the code-snippets given below in to a Python script, say, and run it as


This example is best understood if you have read about the PyRETIS API.

We begin the example by importing from the PyRETIS library:

import numpy as np
from pyretis.core import create_box, Particles, System
from pyretis.simulation import SimulationNVE
from pyretis.engines import VelocityVerlet
from import generate_lattice
from pyretis.forcefield import ForceField
from pyretis.forcefield.potentials import PairLennardJonesCutnp
from matplotlib import pyplot as plt

We also import Numpy and matplotlib here in order to do some plotting. If you want a more fancy plot and have a recent version of matplotlib installed you can try to use one of the many styles, e.g.:'seaborn')

Next, we create the initial positions and use this to set up a simulation box:

print('Creating box:')
xyz, size = generate_lattice('fcc', [3, 3, 3], density=0.9)
size = np.array(size)
box = create_box(low=size[:, 0], high=size[:, 1])

We can use the lattice we generated to populate a system with particles. The system contains information about the box, the particles and the temperature:

print('Creating system:')
system = System(units='lj', box=box, temperature=2.0)
system.particles = Particles(dim=3)
for i, pos in enumerate(xyz):
    system.add_particle(pos, vel=np.zeros_like(pos), force=np.zeros_like(pos),
                        mass=1.0, name='Ar', ptype=0)
gen_settings = {'distribution': 'maxwell', 'momentum': True}

In the last lines in the above code, we generated initial velocities, from a Maxwellian distribution such that the total momentum of the system is zero (requested by setting the key 'momentum' to True in the dictionary).

Now, we just have to set-up a force field:

print('Creating force field:')
potentials = [PairLennardJonesCutnp(dim=3, shift=True, mixing='geometric')]
parameters = [{0: {'sigma': 1, 'epsilon': 1, 'rcut': 2.5}}]
ffield = ForceField('Lennard Jones force field',
                    potential=potentials, params=parameters)
system.forcefield = ffield

select an engine and a simulation to run:

print('Creating simulation:')
engine = VelocityVerlet(0.002)

Now, we are ready to run the simulation and plot the energies as a function of simulation step:

ekin = []
vpot = []
etot = []
step = []
for result in
    if result['cycle']['step'] % 10 == 0:
        print('Step:', result['cycle']['step'])
# Do some plotting:
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(step, ekin, label='Kinetic energy')
ax1.plot(step, etot, label='Total energy')
ax1.plot(step, vpot, label='Potential energy')
ax1.set_xlabel('Step no.')
ax1.set_ylabel('Energy / reduced units')
fig1.savefig('out.png', dpi=150)

This should result in a plot similar to:

Energies obtained from the MD simulation.

Fig. 17 Energies as a function of the time in the MD simulation.