# Tutorial 05: Cubic anisotropy energy term¶

Interactive online tutorial:

Cubic anisotropy energy density is computed as

$$w_{ca} = -K_{1} [(\mathbf{m} \cdot \mathbf{u}_{1})^{2}(\mathbf{m} \cdot \mathbf{u}_{2})^{2} + (\mathbf{m} \cdot \mathbf{u}_{2})^{2}(\mathbf{m} \cdot \mathbf{u}_{3})^{2} + (\mathbf{m} \cdot \mathbf{u}_{1})^{2}(\mathbf{m} \cdot \mathbf{u}_{3})^{2}]$$

where $\mathbf{m}$ is the normalised ($|\mathbf{m}|=1$) magnetisation, $K_{1}$ is the cubic anisotropy constant, and $\mathbf{u}_{1}$ and $\mathbf{u}_{2}$ are the anisotropy axes. Cubic anisotropy energy term tends to align all magnetic moments parallel or antiparallel to one of the three anisotropy axes.

In oommfc, $\mathbf{m}$ is a part of the magnetisation field system.m. Therefore, only cubic anisotropy constant $K_{1}$ and axes $\mathbf{u}_{1}$ and $\mathbf{u}_{2}$ should be provided as input parameters to uniquely define the energy term. All parameters can be constant in space or spatially varying.

## Spatially constant $K_{1}$ and $\mathbf{u}$¶

Let us start by assembling a simple simple simulation where neither $K$ nor $\mathbf{u}$ vary in space. The sample is a "one-dimensional" chain of magnetic moments.

In [1]:
import oommfc as oc
import discretisedfield as df
import micromagneticmodel as mm

p1 = (-10e-9, 0, 0)
p2 = (10e-9, 1e-9, 1e-9)
cell = (1e-9, 1e-9, 1e-9)

region = df.Region(p1=p1, p2=p2)
mesh = df.Mesh(region=region, cell=cell)


The mesh is

In [2]:
# NBVAL_IGNORE_OUTPUT
mesh.k3d()


The system has a Hamiltonian, which consists of only cubic anisotropy energy term.

In [3]:
K = 1e5  # cubic anisotropy constant (J/m**3)
u1 = (0, 0, 1)  # cubic anisotropy axis
u2 = (0, 1, 0)  # cubic anisotropy axis
system = mm.System(name='cubicanisotropy_constant_K_u')
system.energy = mm.CubicAnisotropy(K=K, u1=u1, u2=u2)


We are going to minimise the system's energy using oommfc.MinDriver later. Therefore, we do not have to define the system's dynamics equation. Finally, we need to define the system's magnetisation (system.m). We are going to make it random with $M_\text{s}=8\times10^{5} \,\text{Am}^{-1}$

In [4]:
import random
import discretisedfield as df

Ms = 8e5  # saturation magnetisation (A/m)

def m_fun(pos):
return [2*random.random()-1 for i in range(3)]

system.m = df.Field(mesh, dim=3, value=m_fun, norm=Ms)


The magnetisation, we set is

In [5]:
# NBVAL_IGNORE_OUTPUT
system.m.k3d_vector(color_field=system.m.z)


Now, we can minimise the system's energy by using oommfc.MinDriver.

In [6]:
md = oc.MinDriver()
md.drive(system)

Running OOMMF (ExeOOMMFRunner) [2020/06/30 13:04]... (2.1 s)


We expect that now all magnetic moments are aligned parallel or antiparallel to the anisotropy axes.

In [7]:
# NBVAL_IGNORE_OUTPUT
system.m.k3d_vector(color_field=system.m.z)


## Spatially varying $\mathbf{K}_{1}$¶

There are two different ways how a parameter can be made spatially varying, by using:

1. Dictionary
2. discretisedfield.Field

### Dictionary¶

In order to define a parameter using a dictionary, regions must be defined in the mesh. Regions are defined as a dictionary, whose keys are the strings and values are discretisedfield.Region objects, which take two corner points of the region as input parameters.

In [8]:
p1 = (-10e-9, 0, 0)
p2 = (10e-9, 1e-9, 1e-9)
cell = (1e-9, 1e-9, 1e-9)
subregions = {'region1': df.Region(p1=(-10e-9, 0, 0), p2=(0, 1e-9, 1e-9)),
'region2': df.Region(p1=(0, 0, 0), p2=(10e-9, 1e-9, 1e-9))}
region = df.Region(p1=p1, p2=p2)
mesh = df.Mesh(region=region, cell=cell, subregions=subregions)

In [9]:
# NBVAL_IGNORE_OUTPUT
mesh.k3d_subregions()


Let us say that there is no cubic anisotropy ($K=0$) in region 1, whereas in region 2 it is $K=10^{5} \,\text{Jm}^{-3}$. K is now defined as a dictionary:

In [10]:
K = {'region1': 0, 'region2': 1e5}


The system object is

In [11]:
system = mm.System(name='cubicanisotropy_dict_K')
system.energy = mm.CubicAnisotropy(K=K, u1=u1, u2=u2)
system.m = df.Field(mesh, dim=3, value=m_fun, norm=Ms)


Its magnetisation is

In [12]:
# NBVAL_IGNORE_OUTPUT
system.m.k3d_vector(color_field=system.m.z)


After we minimise the energy

In [13]:
md.drive(system)

Running OOMMF (ExeOOMMFRunner) [2020/06/30 13:04]... (2.5 s)


The magnetisation is as we expected.

In [14]:
# NBVAL_IGNORE_OUTPUT
system.m.k3d_vector(color_field=system.m.z)


### discretisedfield.Field¶

Let us define the spatailly varying uniaxial anisotropy, so that

$K(x, y, z) = \left\{ \begin{array}{ll} 0 & x \le 0 \\ 1e5 & x > 0 \\ \end{array} \right.$

The value of K for the spatially varying anisotropy is set using a Python function.

In [15]:
def K_fun(pos):
x, y, z = pos
if x <= 0:
return 0
else:
return 1e5


The uniaxial anisotropy parameters are

In [16]:
K = df.Field(mesh, dim=1, value=K_fun)


The system is

In [17]:
system = mm.System(name='cubicanisotropy_field_u')
system.energy = mm.CubicAnisotropy(K=K, u1=u1, u2=u2)
system.m = df.Field(mesh, dim=3, value=m_fun, norm=Ms)


and its magnetisation is

In [18]:
# NBVAL_IGNORE_OUTPUT
system.m.k3d_vector(color_field=system.m.z)


After the energy minimisation, the magnetisation is:

In [19]:
# NBVAL_IGNORE_OUTPUT
md.drive(system)
system.m.k3d_vector(color_field=system.m.z)

Running OOMMF (ExeOOMMFRunner) [2020/06/30 13:04]... (2.2 s)