# Micromagnetic standard problem 1¶

Authors: Marijan Beg, Ryan A. Pepper, and Hans Fangohr

Date: 12 December 2016

## Problem specification¶

The simulated sample is a thin film cuboid with dimensions:

• length $$l_{x} = 2 \,\mu\text{m}$$,
• width $$l_{y} = 1 \,\mu\text{m}$$, and
• thickness $$l_{z} = 20 \,\text{nm}$$.

The material parameters (similar to permalloy) are:

• exchange energy constant $$A = 1.3 \times 10^{-11} \,\text{J/m}$$,
• magnetisation saturation $$M_\text{s} = 8 \times 10^{5} \,\text{A/m}$$.

Apart from the symmetric exchange and demagnetisation energies, uniaxial anisotropy energy is also present with

• $$K = 0.5 \times 10^{3} \,\text{J/m}^{3}$$ and
• $$\mathbf{u} = (1, 0, 0)$$.

More details on standard problem 1 specifications can be found in Ref. 1.

## Simulation¶

In the first step, we import the required discretisedfield and oommfc modules.

In [1]:

import discretisedfield as df
import oommfc as oc


Now, we can set all required geometry and material parameters.

In [2]:

# Geometry
lx = 2e-6  # x dimension of the sample(m)
ly = 1e-6  # y dimension of the sample (m)
lz = 20e-9  # sample thickness (m)

# Material parameters
Ms = 8e5  # saturation magnetisation (A/m)
A = 1.3e-11  # exchange energy constant (J/m)
K = 0.5e3  # uniaxial anisotropy constant (J/m**3)
u = (1, 0, 0)  # uniaxial anisotropy axis


Now, we can create a system object with stdprob1 name.

In [3]:

system = oc.System(name="stdprob1")


In order to completely define the micromagnetic system, we need to provide:

1. hamiltonian $$\mathcal{H}$$
2. dynamics $$\text{d}\mathbf{m}/\text{d}t$$
3. magnetisation $$\mathbf{m}$$

Hamiltonian: In the next step, we add energy terms to the Hamiltonian. We add exchange, demagnetisation, and uniaxial anisotropy terms. Although we need to simulate the hysteresis loop with applied magnetic field, we do not add the Zeeman energy contribution to the Hamilotnian because the driver will take care of that.

In [4]:

system.hamiltonian = oc.Exchange(A) + oc.UniaxialAnisotropy(K, u) + oc.Demag()
system.hamiltonian

Out[4]:

$\mathcal{H}=A (\nabla \mathbf{m})^{2}-K_{1} (\mathbf{m} \cdot \mathbf{u})^{2}-\frac{1}{2}\mu_{0}M_\text{s}\mathbf{m} \cdot \mathbf{H}_\text{d}$

Dynamics: In this standard problem, we do not care about the magnetisation dynamics. All we need is the minimum energy magnetisation configuration. Because of that, we will be using the MinDriver which does not need dynamics equation to be specified.

In [5]:

system.dynamics

Out[5]:

$\frac{\partial \mathbf{m}}{\partial t}=0$

Magnetisation: The system is initialised in the $$(10, 1, 0)$$ direction [1]. Firstly, we create the mesh by providing two points p1 and p2 between which the mesh domain spans and the size of a discretisation cell. We choose the discretisation to be $$(20, 20, 20) \,\text{nm}$$.

In [6]:

%matplotlib inline

# Create a mesh object.
mesh = oc.Mesh(p1=(0, 0, 0), p2=(lx, ly, lz), cell=(20e-9, 20e-9, 20e-9))
mesh


Now, the magnetisation field can be fefined.

In [7]:

system.m = df.Field(mesh, value=(-10, -1, 0), norm=Ms)


Hysteresis simulation: Before the hysteresis simulations are carried out, maximum and minimum external magnetic field values at which the system should be relaxed must be created. These values are taken from the standard problem specification [1]. Also, we specify that between $$H_\text{min}$$ and $$H_\text{max}$$ we want $$n=50$$ steps.

In [8]:

Hmax = (50e-3/oc.mu0, 0.87275325e-3/oc.mu0, 0)
Hmin = (-50e-3/oc.mu0, -0.87275325e-3/oc.mu0, 0)
n = 50


Finally, we drive the system using HysteresisDriver.

In [9]:

hd = oc.HysteresisDriver()
hd.drive(system, Hmax=Hmax, Hmin=Hmin, n=n)

2017/5/18 12:48: Calling OOMMF (stdprob1/stdprob1.mif) ... [29.7s]


## Hysteresis loop plot¶

After obtaining the average magnetisation at different external magnetic field values, hysteresis loop is plotted. In this tutorial, we extract the x component of magnetisation and external magnetic field from the system’s datatable and plot it using matplotlib.

In [10]:

%matplotlib inline
import matplotlib.pyplot as plt

Bx = system.dt["Bx"].as_matrix()
mx = system.dt["mx"].as_matrix()

plt.plot(Bx*1e-3, mx)
plt.grid()
plt.xlim([Hmin[0]*oc.mu0, Hmax[0]*oc.mu0])
plt.xlabel("Bx (T)")
plt.ylabel("mx")

Out[10]:

<matplotlib.text.Text at 0x7f1c20a6cef0>


## References¶

[1] µMAG Site Directory: http://www.ctcms.nist.gov/~rdm/mumag.org.html