Python Simulation DPD - Allen-Tildesley/examples GitHub Wiki
DPD program
For the dpd.py example, we recommend generating an initial configuration
using the initialize.py program, with JSON input similar to the following
{"n":100,"density":3.0,"lattice":false,"velocities":true,"soft":true}
The above value of the density is typical when using this method to model water.
For testing we compare with an approximate DPD equation of state for P
- RD Groot, PB Warren, J Chem Phys, 107, 4423 (1997),
- TP Liyana-Arachchi, SN Jamadagni, D Eike, PH Koenig, JI Siepmann, J Chem Phys, 142, 044902 (2015).
The paper of Liyana-Arachchi et al (2015) gives an improvement of the original
EOS of Groot and Warren (1997), which is more accurate and
applicable over a wider range of state points.
The function is included in the dpd program,
and the expected value of P (labelled EOS below)
is printed for comparison at the end.
We give results obtained by both
the Lowe thermostat (L)
and the Shardlow algorithm (S).
Results below were obtained with runs of 10 blocks, each 10000 steps
as for the Fortran examples (program default is 10×1000).
We take the default values of a ρ/T=75, and of other parameters not mentioned below.
| T | ρ | P (EOS) | P (L) | P (S) |
|---|---|---|---|---|
| 0.5 | 3.0 | 11.864 | 11.821(2) | 11.818(1) |
| 1.0 | 3.0 | 23.587 | 23.635(3) | 23.636(2) |
| 1.5 | 3.0 | 35.276 | 35.458(3) | 35.452(4) |
| 2.0 | 3.0 | 46.951 | 47.263(3) | 47.264(4) |
| 1.0 | 2.0 | 14.187 | 14.318(2) | 14.319(2) |
| 1.0 | 4.0 | 32.811 | 32.626(1) | 32.624(2) |
| 1.0 | 5.0 | 41.887 | 41.530(3) | 41.528(2) |