MicroMagnetic.jl

A Julia package for classical spin dynamics and micromagnetic simulations with GPU support.

• Supports classical spin dynamics and micromagnetic simulations.
• Compatible with CPU and multiple GPU platforms, including NVIDIA, AMD, Intel, and Apple GPUs.
• Supports both double and single precision.
• Supports Monte Carlo simulations for atomistic models.
• Implements the Nudged-Elastic-Band method for energy barrier computations.
• Supports Spin-transfer torques, including Zhang-Li and Slonczewski models.
• Incorporates various energy terms and thermal fluctuations.
• Supports constructive solid geometry.
• Supports periodic boundary conditions.
• Easily extensible to add new features.

You don't have to install anything and you can run MicroMagnetic.jl in the cloud via Binder:

All the julia scripts and Jupyter Notebooks (in tutorials folder) hosted in the repository can be executed and modified.

The documentation is available:

Install MicroMagnetic is straightforward as long as Julia (http://julialang.org/downloads/) is installed, and it is equally easy in Windows, Linux and Mac.

In Julia, packages can be easily installed with the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run:

pkg> add MicroMagnetic

Or, equivalently:

julia> using Pkg;
julia> Pkg.add("MicroMagnetic")

To install the latest development version:

pkg> add MicroMagnetic#master

To enable GPU support, one has to install one of the following packages:

For example, we can install CUDA for NVIDIA GPUs:

pkg> add CUDA

Now we will see similar messages if we type using MicroMagnetic

julia> using MicroMagnetic
julia> using CUDA
Precompiling CUDAExt
  1 dependency successfully precompiled in 8 seconds. 383 already precompiled.
[ Info: Switch the backend to CUDA.CUDAKernels.CUDABackend(false, false)

Assuming we have a cylindrical FeGe sample with a diameter of 100 nm and a height of 40 nm, we want to know its magnetization distribution and the stray field around it. We can use the following script:

using MicroMagnetic
@using_gpu() # Import available GPU packages such as CUDA, AMDGPU, oneAPI, or Metal

# Define simulation parameters
args = (
    task = "Relax", # Specify the type of simulation task
    mesh = FDMesh(nx=80, ny=80, nz=30, dx=2e-9, dy=2e-9, dz=2e-9), # Define the mesh grid
    shape = Cylinder(radius=50e-9, height=40e-9), # Define the shape 
    Ms = 3.87e5, # Set the saturation magnetization (A/m)
    A = 8.78e-12, # Set the exchange stiffness constant (J/m)
    D = 1.58e-3, # Set the Dzyaloshinskii-Moriya interaction constant (J/m^2)
    demag = true, # Enable demagnetization effects in the simulation
    m0 = (1,1,1), # Set the initial magnetization direction
    stopping_dmdt = 0.1 # Set the stopping criterion 
);

# Run the simulation with the specified parameters
sim = sim_with(args); 

# Save the magnetization and the stray field into vtk.
save_vtk(sim, "m_demag", fields=["demag"]) 

The magnetization and the stray field around the cylindrical sample are stored in m.vts, which can be opened using Paraview.

If you have any questions about usage, please join the conversation on our GitHub Discussions page.

We greatly appreciate contributions, feature requests, and suggestions! If you encounter any issues or have ideas for improving MicroMagnetic.jl, feel free to open an issue on our GitHub Issues page.

If you use MicroMagnetic.jl in your research, please cite the following publication:

@article{10.1088/1674-1056/ad766f,
  author={Wang, Weiwei and Lyu, Boyao and Kong, Lingyao and Fangohr, Hans and Du, Haifeng},
  title={MicroMagnetic.jl: A Julia package for micromagnetic and atomistic simulations with GPU support},
  journal={Chinese Physics B},
  url={http://iopscience.iop.org/article/10.1088/1674-1056/ad766f},
  year={2024}
}