pylj.mc

Functions related to the Monte-Carlo components of pylj.

pylj.mc.accept(new_energy)[source]

Accept the move.

Parameters:

new_energy (float) – A new total energy for the system.

Returns:

A new total energy for the system.

Return type:

float

pylj.mc.get_new_particle(particles, random_particle, box_length)[source]

Generates a new position for the particle.

Parameters:

  • particles (util.particle.dt, array_like) – Information about the particles.

  • random_particle (int) – Index of the random particle that is selected.

  • box_length (float) – Length of a single dimension of the simulation square.

Returns:

Information about the particles, updated to account for the change of selected particle position.

Return type:

util.particle.dt, array_like

pylj.mc.initialise(number_of_particles, temperature, box_length, init_conf, mass=39.948, constants=[[1.363e-134, 9.273e-78]], forcefield=<class 'pylj.forcefields.lennard_jones'>)[source]

Initialise the particle positions (this can be either as a square or random arrangement), velocities (based on the temperature defined, and # calculate the initial forces/accelerations.

Parameters:

  • number_of_particles (int) – Number of particles to simulate.

  • temperature (float) – Initial temperature of the particles, in Kelvin.

  • box_length (float) – Length of a single dimension of the simulation square, in Angstrom.

  • init_conf (string, optional) – The way that the particles are initially positioned. Should be one of: - ‘square’ - ‘random’

  • mass (float (optional)) – The mass of the particles being simulated.

  • constants (float, array_like (optional)) – The values of the constants for the forcefield used.

  • forcefield (function (optional)) – The particular forcefield to be used to find the energy and forces.

Returns:

System information.

Return type:

System

pylj.mc.initialize(number_particles, temperature, box_length, init_conf)[source]

Maps to the mc.initialise function to account for US english spelling.

pylj.mc.metropolis(temperature, old_energy, new_energy, n=0.7236883483313941)[source]

Determines if the move is accepted or rejected based on the metropolis condition.

Parameters:

  • temperature (float) – Simulation temperature.

  • old_energy (float) – The total energy of the simulation in the previous configuration.

  • new_energy (float) – The total energy of the simulation in the current configuration.

  • n (float, optional) – The random number against which the Metropolis condition is tested. The default is from a numpy uniform distribution.

Returns:

True if the move should be accepted.

Return type:

bool

pylj.mc.reject(position_store, particles, random_particle)[source]

Reject the move and return the particle to the original place.

Parameters:

  • position_store (float, array_like) – The x and y positions previously held by the particle that has moved.

  • particles (util.particle.dt, array_like) – Information about the particles.

  • random_particle (int) – Index of the random particle that is selected.

Returns:

Information about the particles, with the particle returned to the original position

Return type:

util.particle.dt, array_like

pylj.mc.sample(total_energy, system)[source]

Sample parameters of interest in the simulation.

Parameters:

  • total_energy (float) – The total system energy.

  • system (System) – Details about the whole system

Returns:

Details about the whole system, with the new temperature, pressure, msd, and force appended to the appropriate arrays.

Return type:

System

pylj.mc.select_random_particle(particles)[source]

Selects a random particle from the system and return its index and current position.

Parameters:

particles (util.particle.dt, array_like) – Information about the particles.

Returns:

  • int – Index of the random particle that is selected.

  • float, array_like – The current position of the chosen particle.