# Demo#

## Vortex dynamics#

In this example, we are going to simulate vortex core dynamics. After creating a vortex structure, we are first going to displace it by applying an external magnetic field. We will then turn off the external field, and compute the time-development of the system, and then be able to see the dynamics of the vortex core.

The sample is a two-dimensional Permalloy disk sample with $$r=50 \,\text{nm}$$ edge length and $$10\,\text{nm}$$ thickness. Its energy equation consists of ferromagnetic exchange, Zeeman, and demagnetisation energy terms:

$E = \int_{V} \left[-A\mathbf{m}\cdot\nabla^{2}\mathbf{m} - \mu_{0}M_\text{s}\mathbf{m}\cdot\mathbf{H} + w_\text{d}\right] \text{d}V,$

where $$A = 13 \,\text{pJ}\,\text{m}^{-1}$$ is the exchange energy constant, $$M_\text{s} = 8 \times 10^{5} \,\text{A}\,\text{m}^{-1}$$ magnetisation saturation, $$w_\text{d}$$ demagnetisation energy density, $$\mathbf{H}$$ an external magnetic field, and $$\mathbf{m}=\mathbf{M}/M_\text{s}$$ the normalised magnetisation field.

The magnetisation dynamics is governed by the Landau-Lifshitz-Gilbert equation consisting of precession and damping terms:

$\frac{\partial\mathbf{m}}{\partial t} = -\frac{\gamma_{0}}{1+\alpha^{2}}\mathbf{m}\times\mathbf{H}_\text{eff} - \frac{\gamma_{0}\alpha}{1+\alpha^{2}}\mathbf{m}\times(\mathbf{m}\times\mathbf{H}_\text{eff}),$

where $$\gamma_{0} = 2.211 \times 10^{5} \,\text{m}\,\text{A}^{-1}\,\text{s}^{-1}$$ and $$\alpha = 0.05$$ is the Gilbert damping.

The (initial) magnetisation field is a vortex state, whose magnetisation at each point $$(x, y, z)$$ in the sample can be represented as $$(m_{x}, m_{y}, m_{z}) = (-cy, cx, 0.1)$$, with $$c = 10^{-9} \text{m}^{-1}$$.

[1]:

# Some initial configurations
%config InlineBackend.figure_formats = ['svg']  # output matplotlib plots as SVG
import pandas as pd
import matplotlib.pyplot as plt

pd.options.display.max_rows = 5
pd.options.display.float_format = "{:,.2e}".format


## System initialisation#

The Ubermag code for defining the micromagnetic system is:

[2]:

import discretisedfield as df
import micromagneticmodel as mm

# Geometry
r = 50e-9  # Radius of the thin nano magnetic disk (m)
thickness = 10e-9  # sample thickness (m)

# Material (Permalloy) parameters
Ms = 8e5  # saturation magnetisation (A/m)
A = 13e-12  # exchange energy constant (J/m)

# Dynamics (LLG equation) parameters
gamma0 = mm.consts.gamma0  # gyromagnetic ratio (m/As)
alpha = 0.05  # Gilbert damping

system = mm.System(name="vortex_dynamics")

# Energy equation. We omit Zeeman energy term, because H=0.
system.energy = mm.Exchange(A=A) + mm.Demag()

# Dynamics equation
system.dynamics = mm.Precession(gamma0=gamma0) + mm.Damping(alpha=alpha)

# initial magnetisation state
def m_init(point):
x, y, _ = point
c = 1e9  # (1/m)
return (-c * y, c * x, 0.1)

# Defining the geometry of the material as a circular disk
def Ms_func(point):
x, y, _ = point
if x**2 + y**2 <= r**2:
return Ms
else:
return 0

# Sample's centre is placed at origin
region = df.Region(p1=(-r, -r, -thickness / 2), p2=(r, r, thickness / 2))
mesh = df.Mesh(region=region, cell=(5e-9, 5e-9, 10e-9))

system.m = df.Field(mesh, nvdim=3, value=m_init, norm=Ms_func, valid="norm")


The system object is now defined and we can investigate some of its properties:

[3]:

system.energy

[3]:

$- A \mathbf{m} \cdot \nabla^{2} \mathbf{m}-\frac{1}{2}\mu_{0}M_\text{s}\mathbf{m} \cdot \mathbf{H}_\text{d}$
[4]:

system.dynamics

[4]:

$-\frac{\gamma_{0}}{1 + \alpha^{2}} \mathbf{m} \times \mathbf{H}_\text{eff}-\frac{\gamma_{0} \alpha}{1 + \alpha^{2}} \mathbf{m} \times (\mathbf{m} \times \mathbf{H}_\text{eff})$
[5]:

system.m.orientation.sel("z").mpl()


## Energy minimisation#

To carry out micromagnetic simulation, we need to use a micromagnetic calulator. We are going to use OOMMF for this. We can now relax the system in the absence of external magnetic field using energy minimisation driver (MinDriver):

[6]:

import oommfc as oc  # Micromagnetic Calculator

md = oc.MinDriver()
md.drive(system)

system.m.orientation.sel("z").mpl()

Running OOMMF (ExeOOMMFRunner)[2024/08/09 18:15]... (0.4 s)


## Displacement with magnetic field#

Now, we have a relaxed vortex state, with its core at the centre of the sample. As the next step, we want to add an external magnetic field $$H=3.4 \times 10^{4}\,\text{Am}^{-1}$$ in the positive $$x$$-direction to displace the vortex core. We do that by adding the Zeeman energy term to the energy equation:

[7]:

H = (3.4e4, 0, 0)  # an external magnetic field (A/m)

system.energy += mm.Zeeman(H=H)

md.drive(system)
system.m.orientation.sel("z").mpl()

Running OOMMF (ExeOOMMFRunner)[2024/08/09 18:15]... (0.5 s)


## Free relaxation#

The vortex core is now displaced in the positive $$y$$-direction. As the last step, we are going to turn off the external magnetic field and simulate dynamics using TimeDriver. We are going to run simulation for $$20\,\text{ns}$$ and save the magnetisation in $$500$$ steps.

[8]:

system.energy -= mm.Zeeman(H=H)

td = oc.TimeDriver()
td.drive(system, t=20e-9, n=500, verbose=2)

Running OOMMF (ExeOOMMFRunner)[2024/08/09 18:15] took 36.6 s


The final magnetisation state shows that the vortex core has moved back to the sample’s centre.

[9]:

system.m.orientation.sel("z").mpl()


## Data analysis#

### Spatially averaged data#

The table with scalar data saved during the simulation. Each row corresponds to one of the 500 saved configurations. We only show selected columns.

[10]:

system.table.data[["t", "mx", "my", "mz", "E"]].head()

[10]:

t mx my mz E
0 4.00e-11 4.18e-01 5.03e-02 1.82e-02 3.94e-18
1 8.00e-11 4.27e-01 1.92e-01 1.07e-02 3.89e-18
2 1.20e-10 4.26e-01 2.63e-01 3.90e-02 3.86e-18
3 1.60e-10 3.05e-01 2.82e-01 7.84e-03 3.83e-18
4 2.00e-10 2.57e-01 3.97e-01 2.66e-02 3.80e-18

We can now plot the average $$m_{x}$$, $$m_{y}$$ and $$m_{z}$$ values as taken from the table as a function of time to give us an idea of the vortex core position.

[11]:

system.table.mpl(y=["mx", "my", "mz"])


### Spatially resolved data#

Finally, we are going to have a look at the magnetisation field at different time-steps using micromagneticdata.

[12]:

import micromagneticdata as mdata

data = mdata.Data(system.name)
data.info

[12]:

0 0 2024-08-09 18:15:22 MinDriver oommfc None NaN NaN
1 1 2024-08-09 18:15:23 MinDriver oommfc None NaN NaN
2 2 2024-08-09 18:15:23 TimeDriver oommfc None 2.00e-08 5.00e+02

To interactively inspect the time dependent magnetisation, we use data[-1] to refer to the last drive.

[13]:

data[-1].hv(
kdims=["x", "y"], vdims=["x", "y"], scalar_kw={"cmap": "viridis", "clim": (0, Ms)}
)

[13]:


We can now compute winding number using operators from discretisedfield:

$S = \frac{1}{4\pi}\iint q \,\,\text{d}x\text{d}y = \frac{1}{4\pi}\iint\mathbf{m}\cdot\left(\frac{\partial \mathbf{m}}{\partial x} \times \frac{\partial \mathbf{m}}{\partial y~}\right)\text{d}x\text{d}y$
[14]:

import math

m = system.m.orientation.sel("z")
S = m.dot(m.diff("x").cross(m.diff("y"))).integrate() / (4 * math.pi)
S

[14]:

array([0.42290507])


The winding number is commonly used and there is a predefined function in discretisedfield.tools. To get more accurate results we use a different numerical method than just “naively” evaluating the integral.

[15]:

df.tools.topological_charge(system.m.sel("z"), method="berg-luescher")

[15]:

0.5024556893362209


We can also plot the topological charge density in an interactive plot.

[16]:

data[-1].register_callback(
lambda f: df.tools.topological_charge_density(f.sel("z"))
).hv(kdims=["x", "y"])

[16]:


### Trajectory of the vortex core#

We can compute the trajectory of the vortex core via the center of mass of the topological charge:

$\mathbf{R} = \frac{ \int \mathbf{r} \rho(\mathbf{r}) d^2\mathbf{r}}{\int \rho(\mathbf{r}) d^2\mathbf{r}}.$
[17]:

rho = df.tools.topological_charge_density(system.m.sel("z"))
r = system.m.sel("z").mesh.coordinate_field()
R = (r * rho).integrate() / rho.integrate()
R

[17]:

array([6.24115029e-12, 4.54330907e-12])


Now, we need to find the center of the vortex at each time step, this can be achieved by taking the data from last drive.

[18]:

def compute_vortex_centre(drive):
x_coords = []
y_coords = []

r = drive[0].sel("z").mesh.coordinate_field()

for m in drive:
tcd = df.tools.topological_charge_density(m.sel("z"))
centre_of_mass = (r * tcd).integrate() / tcd.integrate()
x_coords.append(centre_of_mass[0])
y_coords.append(centre_of_mass[1])

return pd.DataFrame(
{"t": drive.table.data["t"], "pos x": x_coords, "pos y": y_coords}
)

[19]:

pos_pol_plus = compute_vortex_centre(data[-1])


We can now plot the vortex trajectory on top of the initial configuration.

[20]:

fig, ax = plt.subplots()
data[-1][0].orientation.sel("z").mpl(ax=ax, scalar_kw={"clim": (0, 1)})
ax.plot(pos_pol_plus["pos x"] * 1e9, pos_pol_plus["pos y"] * 1e9, c="yellow")

[20]:

[<matplotlib.lines.Line2D at 0x7f77d042df90>]


Finally, let us delete all simulation files:

[21]:

oc.delete(system)