#### NOTE [Go to the end](#sphx-glr-download-auto-examples-dpd-simulations-plot-dpd-simulation-py) to download the full example code. # Dissipative Particle Dynamics (DPD) Simulations This example demonstrates how to set up and run a DPD simulation with slip-springs using pylimer-tools. References: Langeloth *et al.* [[LMBohmMullerPlathe13](../../acknowledgements.html.md#id11)] and Schneider *et al.* [[SFKarimiVarzanehMullerPlathe21](../../acknowledgements.html.md#id10)]. Assuming your `pylimer_tools` version was compiled with OpenMP, to run this in parallel, you can use the typical OpenMP environment variables, e.g., in the terminal before running your script: `export OMP_NUM_THREADS=4` to use 4 threads. ```Python import os from pathlib import Path import pandas as pd from pylimer_tools.io.read_lammps_output_file import read_data_file from pylimer_tools_cpp import ( AtomStyle, ComputedDoubleValues, ComputedIntValues, DPDSimulator, OutputConfiguration, ) # Load a LAMMPS data file (replace with your file) def _find_fixtures_dir() -> Path: for cur in ( [Path(__file__).resolve().parent] if "__file__" in globals() else [] ) + [Path.cwd()]: for p in [cur] + list(cur.parents): candidate = p / "tests" / "pylimer_tools" / "fixtures" if candidate.is_dir(): return candidate return Path("tests/pylimer_tools/fixtures").resolve() _fixtures_dir = _find_fixtures_dir() filePath = str(_fixtures_dir / "structure/melt_83_a_100.structure.out") universe = read_data_file( filePath, atom_style=[AtomStyle.HYBRID, AtomStyle.BOND, AtomStyle.EDPD] ) # Create the DPD simulator dpd_simulator = DPDSimulator( universe=universe, seed="12345", # Set a random seed for reproducibility* ) ``` Note that the random seed does not guarantee the same results across different runs, as the DPD algorithm is inherently stochastic and will produce different results if you use more than one processor. ```Python # Randomly sample slip-springs dpd_simulator.create_slip_springs(num=100) # Configure what to output during the simulation on each step step_output_config = OutputConfiguration() step_output_config.int_values = [ ComputedIntValues.STEP, # Output the simulation step ] step_output_config.double_values = [ ComputedDoubleValues.TEMPERATURE, # Output the temperature ComputedDoubleValues.PRESSURE, # Output the pressure ComputedDoubleValues.STRESS_XX, # Output the xx component of the stress tensor ComputedDoubleValues.STRESS_YY, # Output the yy component of the stress tensor ComputedDoubleValues.STRESS_ZZ, # Output the zz component of the stress tensor ] step_output_config.output_every = 10 # Output every 10 steps # temporary filename for output step_output_config.filename = "dpd_simulation_output.txt" dpd_simulator.config_step_output([step_output_config]) # Configure what to average during the simulation avg_output_config = OutputConfiguration() avg_output_config.int_values = [ ComputedIntValues.STEP, # Output the simulation step ] avg_output_config.double_values = [ ComputedDoubleValues.TEMPERATURE, # Output the temperature ComputedDoubleValues.PRESSURE, # Output the pressure ] avg_output_config.output_every = 10 step_output_config.use_every = 1 avg_output_config.filename = "dpd_simulation_avg_output.txt" dpd_simulator.config_average_output([avg_output_config]) dpd_simulator.run_simulation( n_steps=1000, # Number of simulation steps dt=0.01, # Time step size with_MC=True, # Enable Monte Carlo moves of slip-springs ) print("DPD simulation completed.") ``` ```none DPD simulation completed. ``` read the output file and actually plot something ```Python df = pd.read_csv("dpd_simulation_output.txt", sep="\\s+") _ = df.plot( x="Step", y=[ "Temperature", "Pressure", "Stress[0,0]", "Stress[1,1]", "Stress[2,2]", ], subplots=True, layout=(3, 2), figsize=(10, 10), ) ``` **Total running time of the script:** (0 minutes 3.146 seconds) [`Download Jupyter notebook: plot_dpd_simulation.ipynb`](_downloads/7ea8883241a6011e02c758aef99af3f6/plot_dpd_simulation.ipynb) [`Download Python source code: plot_dpd_simulation.py`](_downloads/1c1487cbd2deec0433f6e971e8ec5acf/plot_dpd_simulation.py) [`Download zipped: plot_dpd_simulation.zip`](_downloads/fc465a2cb3da2f85f7d05c08404f3566/plot_dpd_simulation.zip) [Gallery generated by Sphinx-Gallery](https://sphinx-gallery.github.io)