14. Electrokinetics¶

The electrokinetics setup in ESPResSo allows for the description of electro-hydrodynamic systems on the level of ion density distributions coupled to a lattice-Boltzmann (LB) fluid. The ion density distributions may also interact with explicit charged particles, which are interpolated on the LB grid. In the following paragraph we briefly explain the electrokinetic model implemented in ESPResSo, before we come to the description of the interface.

Note

Please cite Godenschwager et al. [2013] and Bauer et al. [2021] (BibTeX keys godenschwager13a and bauer21a in doc/bibliography.bib) if you use the LB fluid. When generating your own kernels with pystencils and lbmpy, please also cite Bauer et al. [2019] and Bauer et al. [2021] (BibTeX key bauer19a resp. bauer21b in doc/bibliography.bib).

Note

Requires external features WALBERLA and optionally WALBERLA_FFT (for the FFT-based Poisson solver), enabled with the CMake options -D ESPRESSO_BUILD_WITH_WALBERLA=ON -D ESPRESSO_BUILD_WITH_WALBERLA_FFT=ON.

14.1. Electrokinetic equations¶

In the electrokinetics code we solve the following system of coupled continuity, diffusion-advection, Poisson, and Navier-Stokes equations:

\begin{split}\begin{aligned} \label{eq:ek-model-continuity} \frac{\partial n_k}{\partial t} & = & -\, \nabla \cdot \vec{j}_k \vphantom{\left(\frac{\partial}{\partial}\right)} ; \\ \label{eq:ek-model-fluxes} \vec{j}_{k} & = & -D_k \nabla n_k - \nu_k \, q_k n_k\, \nabla \Phi + n_k \vec{v}_{\mathrm{fl}} \vphantom{\left(\frac{\partial}{\partial}\right)} + \sqrt{n_k}\vec{\mathcal{W}}_k; \\ \label{eq:ek-model-poisson} \Delta \Phi & = & -4 \pi \, {l_\mathrm{B}}\, {k_\mathrm{B}T}\sum_k q_k n_k \vphantom{\left(\frac{\partial}{\partial}\right)}; \\ \nonumber \left(\frac{\partial \vec{v}_{\mathrm{fl}}}{\partial t} + \vec{v}_{\mathrm{fl}} \cdot \nabla \vec{v}_{\mathrm{fl}} \right) \rho_\mathrm{fl} & = & -{k_\mathrm{B}T}\, \nabla \rho_\mathrm{fl} - q_k n_k \nabla \Phi \\ \label{eq:ek-model-velocity} & & +\, \eta \Delta \vec{v}_{\mathrm{fl}} + (\eta / 3 + \eta_{\text{b}}) \nabla (\nabla \cdot \vec{v}_{\mathrm{fl}}) \vphantom{\left(\frac{\partial}{\partial}\right)} ; \\ \label{eq:ek-model-continuity-fl} \frac{\partial \rho_\mathrm{fl}}{\partial t} & = & -\,\nabla\cdot\left( \rho_\mathrm{fl} \vec{v}_{\mathrm{fl}} \right) \vphantom{\left(\frac{\partial}{\partial}\right)} , \end{aligned}\end{split}

which define relations between the following observables

$$n_k$$

the number density of the particles of species $$k$$,

$$\vec{j}_k$$

the number density flux of the particles of species $$k$$,

$$\Phi$$

the electrostatic potential,

$$\rho_{\mathrm{fl}}$$

the mass density of the fluid,

$$\vec{v}_{\mathrm{fl}}$$

the advective velocity of the fluid,

and input parameters

$$D_k$$

the diffusion constant of species $$k$$,

$$\nu_k$$

the mobility of species $$k$$,

$$\vec{\mathcal{W}}_k$$

the white-noise term for the fluctuations of species $$k$$,

$$q_k$$

the charge of a single particle of species $$k$$,

$${l_\mathrm{B}}$$

the Bjerrum length,

$${k_\mathrm{B}T}$$
the thermal energy given by the product of Boltzmann’s constant $$k_\text{B}$$
and the temperature $$T$$,
$$\eta$$

the dynamic viscosity of the fluid,

$$\eta_{\text{b}}$$

the bulk viscosity of the fluid.

The temperature $$T$$, and diffusion constants $$D_k$$ and mobilities $$\nu_k$$ of individual species are linked through the Einstein-Smoluchowski relation $$D_k / \nu_k = {k_\mathrm{B}T}$$. This system of equations combining diffusion-advection, electrostatics, and hydrodynamics is conventionally referred to as the Electrokinetic Equations.

The electrokinetic equations have the following properties:

• On the coarse time and length scale of the model, the dynamics of the particle species can be described in terms of smooth density distributions and potentials as opposed to the microscale where highly localized densities cause singularities in the potential.

In most situations, this restricts the application of the model to species of monovalent ions, since ions of higher valency typically show strong condensation and correlation effects – the localization of individual ions in local potential minima and the subsequent correlated motion with the charges causing this minima.

• Only the entropy of an ideal gas and electrostatic interactions are accounted for. In particular, there is no excluded volume.

This restricts the application of the model to monovalent ions and moderate charge densities. At higher valencies or densities, overcharging and layering effects can occur, which lead to non-monotonic charge densities and potentials, that can not be covered by a mean-field model such as Poisson–Boltzmann or this one.

Even in salt free systems containing only counter ions, the counter-ion densities close to highly charged objects can be overestimated when neglecting excluded volume effects. Decades of the application of Poisson–Boltzmann theory to systems of electrolytic solutions, however, show that those conditions are fulfilled for monovalent salt ions (such as sodium chloride or potassium chloride) at experimentally realizable concentrations.

• Electrodynamic and magnetic effects play no role. Electrolytic solutions fulfill those conditions as long as they don’t contain magnetic particles.

• The diffusion coefficient is a scalar, which means there can not be any cross-diffusion. Additionally, the diffusive behavior has been deduced using a formalism relying on the notion of a local equilibrium. The resulting diffusion equation, however, is known to be valid also far from equilibrium.

• The temperature is constant throughout the system.

• The density fluxes instantaneously relax to their local equilibrium values. Obviously one can not extract information about processes on length and time scales not covered by the model, such as dielectric spectra at frequencies, high enough that they correspond to times faster than the diffusive time scales of the charged species.

14.2. Setup¶

14.2.1. Initialization¶

Here is a minimal working example:

import espressomd
import espressomd.electrokinetics

system = espressomd.System(box_l=3 * [6.0])
system.time_step = 0.01
system.cell_system.skin = 1.0

lattice = espressomd.electrokinetics.LatticeWalberla(agrid=0.5, n_ghost_layers=1)
ek_solver = espressomd.electrokinetics.EKNone(lattice=lattice)
system.ekcontainer = espressomd.electrokinetics.EKContainer(
solver=ek_solver, tau=system.time_step)


where system.ekcontainer is the EK system, ek_solver is the Poisson solver (here EKNone doesn’t actually solve the electrostatic field, but instead imposes a zero field), and lattice contains the grid parameters. In this setup, the EK system doesn’t contain any species. The following sections will show how to add species that can diffuse, advect, react and/or electrostatically interact. An EK system can be set up at the same time as a LB system.

To detach an EK system, use the following syntax:

system.ekcontainer = None


14.2.2. Diffusive species¶

ek_species = espressomd.electrokinetics.EKSpecies(
lattice=lattice,
single_precision=False,
kT=1.0,
tau=system.time_step,
density=0.85,
valency=0.0,
diffusion=0.1,
friction_coupling=False,
ext_efield=[0., 0., 0.]
)


EKSpecies is used to initialize a diffusive species. Here the options specify: the electrokinetic number densities density (independent from the LB density), the diffusion coefficient diffusion, the valency of the particles of that species valency, the optional external (electric) force ext_efield which is applied to the diffusive species, the thermal energy kT for thermal fluctuations, friction_coupling to enable coupling of the diffusive species to the LB fluid force and advection to add an advective contribution to the diffusive species’ fluxes from the LB fluid. Multiple species can be added to the EK system.

To add species to the EK system:

system.ekcontainer.add(ek_species)


To remove species from the EK system:

system.ekcontainer.remove(ek_species)


Individual nodes and slices of the species lattice can be accessed and modified using the syntax outlined in Reading and setting properties of single lattice nodes.

As mentioned before, the LB density is completely decoupled from the electrokinetic densities. This has the advantage that greater freedom can be achieved in matching the internal parameters to an experimental system. Moreover, it is possible to choose parameters for which the LB is more stable.

14.2.3. Performance considerations¶

The CPU implementation of the EK has an extra flag single_precision to use single-precision floating point values. These are approximately 10% faster than double-precision, at the cost of a small loss in precision.

14.3. Checkpointing¶

ek_species.save_checkpoint(path, binary)


The first command saves all of the EK nodes’ properties to an ASCII (binary=False) or binary (binary=True) format respectively. The second command loads the EK nodes’ properties. In both cases path specifies the location of the checkpoint file. This is useful for restarting a simulation either on the same machine or a different machine. Some care should be taken when using the binary format as the format of doubles can depend on both the computer being used as well as the compiler.

14.4. VTK output¶

The waLBerla library implements a globally-accessible VTK registry. A VTK stream can be attached to an EK actor to periodically write one or multiple fluid field data into a single file using VTKOutput:

vtk_obs = ["density"]
# create a VTK callback that automatically writes every 10 EK steps
ek_vtk = espressomd.electrokinetics.VTKOutput(
identifier="ek_vtk_automatic", observables=vtk_obs, delta_N=10)
system.integrator.run(100)
# can be deactivated
ek_vtk.disable()
system.integrator.run(10)
ek_vtk.enable()
# create a VTK callback that writes only when explicitly called
ek_vtk_on_demand = espressomd.electrokinetics.VTKOutput(
identifier="ek_vtk_now", observables=vtk_obs)
ek_vtk_on_demand.write()


Currently only supports the species density. By default, the properties of the current state of the species are written to disk on demand. To add a stream that writes to disk continuously, use the optional argument delta_N to indicate the level of subsampling. Such a stream can be deactivated.

The VTK format is readable by visualization software such as ParaView 5 or Mayavi2 6, as well as in ESPResSo (see Reading VTK files). If you plan to use ParaView for visualization, note that also the particle positions can be exported using the VTK format (see writevtk()).

Important: these VTK files are written in multi-piece format, i.e. each MPI rank writes its local domain to a new piece in the VTK uniform grid to avoid a MPI reduction. ParaView can handle the topology reconstruction natively. However, when reading the multi-piece file with the Python vtk package, the topology must be manually reconstructed. In particular, calling the XML reader GetOutput() method directly after the update step will erase all topology information. While this is not an issue for VTK files obtained from simulations that ran with 1 MPI rank, for parallel simulations this will lead to 3D grids with incorrectly ordered data. Automatic topology reconstruction is available through VTKReader:

import pathlib
import tempfile
import numpy as np
import espressomd
import espressomd.electrokinetics
import espressomd.io.vtk

system = espressomd.System(box_l=[12., 14., 10.])
system.cell_system.skin = 0.4
system.time_step = 0.1

lattice = espressomd.electrokinetics.LatticeWalberla(agrid=1., n_ghost_layers=1)
ek_solver = espressomd.electrokinetics.EKNone(lattice=lattice)
ek_species = espressomd.electrokinetics.EKSpecies(
lattice=lattice, density=1., kT=1., diffusion=0.1, valency=0.,
system.ekcontainer = espressomd.electrokinetics.EKContainer(
solver=ek_solver, tau=ek_species.tau)
system.integrator.run(10)

label_density = "density"

with tempfile.TemporaryDirectory() as tmp_directory:
path_vtk_root = pathlib.Path(tmp_directory)
label_vtk = "ek_vtk"
path_vtk = path_vtk_root / label_vtk / "simulation_step_0.vtu"

# write VTK file
ek_vtk = espressomd.electrokinetics.VTKOutput(
identifier=label_vtk, delta_N=0,
observables=["density"],
base_folder=str(path_vtk_root))
ek_vtk.write()

vtk_density = vtk_grids[label_density]

# check VTK values match node values
ek_density = np.copy(ek_species[:, :, :].density)
np.testing.assert_allclose(vtk_density, ek_density, rtol=1e-10, atol=0.)


14.5. Setting up boundary conditions¶

It is possible to impose a fixed density and a fixed flux on EK species.

Under the hood, a boundary field is added to the blockforest, which contains pre-calculated information for the streaming operations.

14.5.1. Per-node boundary conditions¶

One can set (or update) the boundary conditions of individual nodes:

import espressomd
import espressomd.electrokinetics
system = espressomd.System(box_l=[10.0, 10.0, 10.0])
system.cell_system.skin = 0.1
system.time_step = 0.01
lattice = espressomd.electrokinetics.LatticeWalberla(agrid=0.5, n_ghost_layers=1)
ek_solver = espressomd.electrokinetics.EKNone(lattice=lattice)
ek_species = espressomd.electrokinetics.EKSpecies(
kT=1.5, lattice=lattice, density=0.85, valency=0., diffusion=0.1,
system.ekcontainer = espressomd.electrokinetics.EKContainer(
solver=ek_solver, tau=ek_species.tau)
# set node fixed density boundary conditions
ek_species[0, 0, 0].boundary = espressomd.electrokinetics.DensityBoundary(1.)
# update node fixed density boundary conditions
ek_species[0, 0, 0].boundary = espressomd.electrokinetics.DensityBoundary(2.)
# remove node boundary conditions
ek_species[0, 0, 0].boundary = None


14.5.2. Shape-based boundary conditions¶

Adding a shape-based boundary is straightforward:

import espressomd
import espressomd.electrokinetics
import espressomd.shapes
system = espressomd.System(box_l=[10.0, 10.0, 10.0])
system.cell_system.skin = 0.1
system.time_step = 0.01
lattice = espressomd.electrokinetics.LatticeWalberla(agrid=0.5, n_ghost_layers=1)
ek_solver = espressomd.electrokinetics.EKNone(lattice=lattice)
ek_species = espressomd.electrokinetics.EKSpecies(
kT=1.5, lattice=lattice, density=0.85, valency=0.0, diffusion=0.1,
system.ekcontainer = espressomd.electrokinetics.EKContainer(
solver=ek_solver, tau=ek_species.tau)
# set fixed density boundary conditions
wall = espressomd.shapes.Wall(normal=[1., 0., 0.], dist=2.5)
shape=wall, value=1., boundary_type=espressomd.electrokinetics.DensityBoundary)
# clear fixed density boundary conditions
ek_species.clear_density_boundaries()


For a position-dependent flux, the argument to value must be a 4D grid (the first three dimensions must match the EK grid shape, the fourth dimension has size 3 for the flux).

For a complete description of all available shapes, refer to espressomd.shapes.

14.6. Prototyping new EK methods¶

Start by installing the code generator dependencies:

python3 -m pip install --user -c requirements.txt numpy sympy lbmpy pystencils islpy


Next, edit the code generator script to configure new kernels, then execute it:

python3 maintainer/walberla_kernels/generate_lb_kernels.py


The script takes optional arguments to control the CPU or GPU architecture, as well as the floating-point precision. The generated source code files need to be written to src/walberla_bridge/src/electrokinetics/generated_kernels/ and src/walberla_bridge/src/electrokinetics/reactions/generated_kernels/. These steps can be automated with the convenience shell functions documented in maintainer/walberla_kernels/Readme.md. Edit the CMakeLists.txt file in the destination folders to include the new kernels in the build system. Then, adapt src/walberla_bridge/src/electrokinetics/EKinWalberlaImpl.hpp to use the new EK kernels.

5

https://www.paraview.org/

6

http://code.enthought.com/projects/mayavi/