Particlesim

Simple tool for particle markov simulations and simulated annealing using Ewald Summation.

Setup

To install the package, simply go to the package directory and run python setup.py install with in terminal.

Initialization

There are basically two ways to initialize a simulation:

Using a csv input file

• The basic parameters are set in a config file that looks like this

[general]
k_cutoff = 1.0
sigma_ewald = 1.0
r_cutoff = 3.0
box-size = 20.0

[manual]
csv_path = /path/to/csvfile/example_init_positions.
[ewald_summation]
use_ewald = yes
sigma = 1.0

[lennard_jones]
use_lennard_jones = yes

• The initial particle configuration is defined by a csv file at the location given in the config and should look similar to this example

label,x,y,z,Charge,LJ-Epsilon,LJ-Sigma
Na,1,0,0,1,0.5,1
Cl,2,2,1,-1,0.5,3
Cl,1,0,1,-1,1,1
Na,1,1,1,1,0.5,1

• Then the following commands are necessary to initialize the setup

# Get a ProblemCreator file from the config file
creator = ProblemCreator(test_config_path)

# Initialize a SystemConfiguration Object from the config file
system_config = creator.generate_problem()

Using a config input file

• The basic parameters are set in a config file similar to the previous case but without a [manual] section. But instead, it contains sections for each particle type beginning with particle_class_*. Each class will be collected in the following.

[general]
k_cutoff = 1.0
Sigma_ewald = 1.0
r_cutoff = 3.0
box-size = 20.0

[particle_class_1]
label = Natrium
type = NA
charge = 1
distribution = uniform
number = 10

[particle_class_2]
label = Chlor
type = CL
charge = -1
distribution = uniform
number = 20

The Lennard-Jones parameters for the atom types are defined in lib/particle_types.py. This file can be edited to fit your purpose. In the following, a particle setup is created according to your statements for the attributes distribution and number. (At this point, only the creation of uniform particle distributions is supported.)

• The initialization process is equal to the previous method

# Get a ProblemCreator file from the config file
creator = ProblemCreator(test_config_path)

# Initialize a SystemConfiguration Object from the config file
system_config = creator.generate_problem()

Run

To run the simulation simply create a sampler with

sampler = particlesim.api.Sampler(system_configuration=system_conf)

And run one simple metropolis simulation with

xyz_trajectory, potential = sampler.metropolis(iteration_number=1000,beta=100)

Or metropolis with simulated annealing

xyz_trajectory, potential = sampler.metropolis(iteration_number=1000, step=0.1, beta=100)

beta can be a single value - in this case it gets interpreted as the maximum value -, a tuple that defines the minimum and maximum value or a list with a length of iteration_number to define a beta value for each simulation step.

Evaluation

At this point a trajectory export to xyz-Format and csv output is supported and can be accessed with the particlesim.utils.xyz and particlesim.config_parser module.