10. Magnetostatics

10.1. Dipolar interaction

ESPResSo contains methods to calculate the interactions between point dipoles

\[U^{Dip}(\vec{r}) = D \cdot \left( \frac{(\vec{\mu}_i \cdot \vec{\mu}_j)}{r^3} - \frac{3 (\vec{\mu}_i \cdot \vec{r}) (\vec{\mu}_j \cdot \vec{r}) }{r^5} \right)\]

where \(r=|\vec{r}|\). The prefactor \(D\) is can be set by the user and is given by

(1)\[D =\frac{\mu_0 \mu}{4\pi}\]

where \(\mu_0\) and \(\mu\) are the vacuum permittivity and the relative permittivity of the background material, respectively.

Magnetostatic interactions are activated when attached to the system:

import espressomd
import espressomd.magnetostatics

system = espressomd.System(box_l=[10, 10, 10])
system.time_step = 0.01
system.part.add(pos=[[0, 0, 0], [1, 1, 1]], dip=2 * [(1, 0, 0)],
                rotation=2 * [(True, True, True)])

actor = espressomd.magnetostatics.DipolarDirectSumCpu(prefactor=1.)
system.magnetostatics.solver = actor

The solver can be detached with either:

system.magnetostatics.solver = None



10.1.1. Dipolar P3M


This is the dipolar version of the P3M algorithm, described in [Cerdà et al., 2008].

Make sure that you know the relevance of the P3M parameters before using P3M! If you are not sure, read the following references: [Cerdà et al., 2008, Deserno, 2000, Deserno and Holm, 1998, Deserno and Holm, 1998, Deserno et al., 2000, Ewald, 1921, Hockney and Eastwood, 1988, Kolafa and Perram, 1992].

Note that dipolar P3M does not work with non-cubic boxes.

The parameters of the dipolar P3M method can be tuned automatically, by providing accuracy=<TARGET_ACCURACY> to the method. It is also possible to pass a subset of the method parameters such as mesh. In that case, only the omitted parameters are tuned:

import espressomd.magnetostatics as magnetostatics
p3m = magnetostatics.DipolarP3M(prefactor=1, mesh=32, accuracy=1E-4)
system.magnetostatics.solver = p3m

It is important to note that the error estimates given in [Cerdà et al., 2008] used in the tuning contain assumptions about the system. In particular, a homogeneous system is assumed. If this is no longer the case during the simulation, actual force and torque errors can be significantly larger.

10.1.2. Dipolar Layer Correction (DLC)


The dipolar layer correction (DLC) is used in conjunction with the dipolar P3M method to calculate dipolar interactions in a 2D-periodic system. It is based on [Bródka, 2004] and the dipolar version of Electrostatic Layer Correction (ELC).

Usage notes:

  • The non-periodic direction is always the z-direction.

  • The method relies on a slab of the simulation box perpendicular to the z-direction not to contain particles. The size in z-direction of this slab is controlled by the gap_size parameter. The user has to ensure that no particles enter this region by means of constraints or by fixing the particles’ z-coordinate. When particles enter the slab of the specified size, an error will be thrown.

  • The method can be tuned using the accuracy parameter. In contrast to the electrostatic method, it refers to the energy. Furthermore, it is assumed that all dipole moment are as large as the largest of the dipoles in the system.

  • When the base solver is not a P3M method, metallic epsilon is assumed.

The method is used as follows:

import espressomd.magnetostatics
dp3m = espressomd.magnetostatics.DipolarP3M(prefactor=1, accuracy=1E-4)
mdlc = espressomd.magnetostatics.DLC(actor=dp3m, maxPWerror=1E-5, gap_size=2.)
system.magnetostatics.solver = mdlc

10.2. Dipolar direct sum

This interaction calculates energies and forces between dipoles by explicitly summing over all pairs. For the directions in which the system is periodic (as defined by system.periodicity), it applies the minimum image convention, i.e. the interaction is effectively cut off at half a box length.

The direct summation methods are mainly intended for non-periodic systems which cannot be solved using the dipolar P3M method. Due to the long-range nature of dipolar interactions, direct summation with minimum image convention does not yield good accuracy with periodic systems.

Two methods are available:

  • DipolarDirectSumCpu performs the calculation in double-precision on the CPU, optionally with replicas.

  • DipolarDirectSumGpu performs the calculations in single-precision on a CUDA-capable GPU. The implementation is optimized for large systems of several thousand particles. It makes use of one thread per particle. When there are fewer particles than the number of threads the GPU can execute simultaneously, the rest of the GPU remains idle. Hence, the method will perform poorly for small systems.

To use the methods, create an instance of either DipolarDirectSumCpu or DipolarDirectSumGpu and attach it to the system. The only required parameter is the prefactor (1):

import espressomd.magnetostatics
dds = espressomd.magnetostatics.DipolarDirectSumGpu(prefactor=1)
system.magnetostatics.solver = dds

The CPU implementation has an optional argument n_replicas which adds periodic copies to the system along periodic directions. In that case, the minimum image convention is no longer used. Additionally, enabling the DIPOLE_FIELDS_TRACKING feature enables the CPU implementation to calculate the total dipole field at the position of each magnetic particle in the primary simulation box. These values are stored in the particle handle’s dip_fld property and can be accessed directly or via an observable.

Both the CPU and GPU implementations support MPI-parallelization.

10.3. Barnes-Hut octree sum on GPU


This interaction calculates energies and forces between dipoles by summing over the spatial octree cells (aka leaves). Far enough cells are considered as a single dipole with a cumulative vector in the cell center of mass. Parameters which determine that the cell is far enough are \(I_{\mathrm{tol}}^2\) and \(\varepsilon^2\) which define a fraction of the cell and an additive distance respectively. For the detailed description of the Barnes-Hut method application to the dipole-dipole interactions, please refer to [Polyakov et al., 2013].

To use the method, create an instance of DipolarBarnesHutGpu and attach it to the system:

import espressomd.magnetostatics
bh = espressomd.magnetostatics.DipolarBarnesHutGpu(prefactor=1., epssq=200.0, itolsq=8.0)
system.magnetostatics.solver = bh

10.4. ScaFaCoS magnetostatics


ESPResSo can use the methods from the ScaFaCoS Scalable fast Coulomb solvers library for dipoles, if the methods support dipolar calculations. The feature SCAFACOS_DIPOLES has to be added to myconfig.hpp to activate this feature. Dipolar calculations are only included in the dipoles branch of the ScaFaCoS code. The specific methods available can be queried with espressomd.electrostatics.Scafacos.get_available_methods().

To use ScaFaCoS, create an instance of Scafacos and attach it to the system. Three parameters have to be specified: prefactor, method_name, method_params. The method-specific parameters are described in the ScaFaCoS manual. In addition, methods supporting tuning have a parameter tolerance_field which sets the desired root mean square accuracy for the magnetic field.

Here is an example with the P2NFFT method for 2D-periodic systems:

import espressomd.magnetostatics
scafacos = espressomd.magnetostatics.Scafacos(
    prefactor=1., method_name="p2nfft",
        "p2nfft_verbose_tuning": 0,
        "pnfft_N": "80,80,160",
        "pnfft_window_name": "bspline",
        "pnfft_m": "4",
        "p2nfft_ignore_tolerance": "1",
        "pnfft_diff_ik": "0",
        "p2nfft_r_cut": "6",
        "p2nfft_alpha": "0.8",
        "p2nfft_epsB": "0.05"})
system.magnetostatics.solver = scafacos

For details of the various methods and their parameters please refer to the ScaFaCoS manual. To use this feature, ScaFaCoS has to be built as a shared library.