OOMMF

Official Resources

• Homepage: https://math.nist.gov/oommf/
• Download: https://math.nist.gov/oommf/software.html
• Documentation: https://math.nist.gov/oommf/doc/userguide20a3/userguide/
• License: Public domain (NIST)

Overview

OOMMF (Object Oriented MicroMagnetic Framework) is a public domain micromagnetic simulation program developed at NIST. It solves the Landau-Lifshitz-Gilbert equation on a finite-difference grid using Tcl/Tk for GUI and C++ for computation, and is the de facto standard for micromagnetic benchmark problems.

Scientific domain: Micromagnetic simulation, domain dynamics, magnetic recording
Target user community: Researchers simulating magnetization dynamics in nanostructures, thin films, and magnetic devices

Theoretical Methods

• Landau-Lifshitz-Gilbert (LLG) equation
• Finite-difference method
• Exchange energy
• Zeeman energy
• Magnetostatic (demagnetization) energy
• Magnetocrystalline anisotropy energy
• Surface anisotropy
• Dzyaloshinskii-Moriya interaction (via extensions)

Capabilities (CRITICAL)

• Micromagnetic simulation (time-domain LLG)
• Energy minimization (relaxation to ground state)
• Hysteresis loop calculation
• Domain wall dynamics
• Vortex and skyrmion simulation
• Standard problem benchmarks (µMAG)
• Tcl/Tk graphical interface
• Extensible via Oxs (OOMMF eXtensible Solver) child classes
• Batch simulation mode
• 2D and 3D geometries

Sources: NIST OOMMF documentation, µMAG standard problems

Key Strengths

NIST Standard:

• Reference implementation for micromagnetics
• µMAG standard problem validation
• Public domain (no licensing restrictions)
• Decades of validation and testing

Extensible Architecture:

• Oxs child class system
• Custom energy terms
• Custom evolvers
• Tcl scripting for automation
• Python interfaces (via extensions)

Comprehensive Physics:

• All standard energy terms
• Accurate demagnetization field
• Multiple integration methods
• Thermal fluctuations support

GUI and Scripting:

• Interactive Tcl/Tk GUI
• Batch scripting capability
• Real-time visualization
• Parameter sweeps

Inputs & Outputs

• Input formats:

  • OMF/OVF vector field files
  • MIF (OOMMF Material Input Format)
  • Tcl script files

• Output data types:

  • Magnetization vector fields (OMF/OVF)
  • Energy vs time
  • Hysteresis loops
  • Average magnetization
  • Field files

Interfaces & Ecosystem

• joommf: Jupyter/OOMMF Python interface
• oommf-extension-dmi-t: DMI extension
• oommf-python: Python wrapper
• µMAG: Standard problem benchmarks

Performance Characteristics

• Speed: Moderate (CPU-based, no GPU)
• Accuracy: High (validated against benchmarks)
• System size: Millions of cells
• Parallelization: Limited (mostly serial)

Computational Cost

• Small systems: Minutes
• Large systems: Hours to days
• Typical: Moderate (no GPU acceleration)

Limitations & Known Constraints

• No GPU: CPU-only computation
• Finite differences only: No finite elements
• Regular grids only: No adaptive meshing
• Tcl/Tk dependency: Required for GUI
• No native DMI: Requires extension
• Limited parallelization: Mostly serial

Comparison with Other Codes

• vs Mumax3: OOMMF is CPU, Mumax3 is GPU; OOMMF is NIST standard
• vs magnum.af: OOMMF is finite-difference, magnum.af supports FEM
• vs MicroMagnetic.jl: OOMMF is Tcl/C++, MicroMagnetic.jl is Julia/GPU
• Unique strength: NIST standard micromagnetic code, public domain, extensive validation, extensible Oxs architecture

Application Areas

Magnetic Recording:

• Write head dynamics
• Media switching
• Bit patterned media
• Heat-assisted recording

Domain Dynamics:

• Domain wall motion
• Vortex dynamics
• Skyrmion creation/annihilation
• Domain wall pinning

Thin Film Magnetism:

• Permalloy films
• Multilayer systems
• Exchange bias
• Interlayer coupling

Standard Problems:

• µMAG benchmarks
• Method validation
• Code comparison
• Teaching tool

Best Practices

Mesh Selection:

• Use cell size ≤ exchange length
• Test convergence with cell size
• Consider geometry discretization
• Use appropriate boundary conditions

Integration:

• Choose appropriate time step
• Monitor energy conservation
• Use Runge-Kutta for accuracy
• Validate against standard problems

Extension Usage:

• Install DMI extension for chiral magnets
• Use joommf for Python integration
• Check extension compatibility
• Validate extended physics

Community and Support

• Public domain (NIST)
• Extensive documentation
• µMAG community benchmarks
• Active mailing list
• Multiple Python wrapper projects

Verification & Sources

Primary sources:

1. NIST OOMMF: https://math.nist.gov/oommf/
2. M. J. Donahue and D. G. Porter, NISTIR 6376 (1999)
3. µMAG benchmarks: https://www.ctcms.nist.gov/~rdm/mumag.org.html

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Official homepage: ACCESSIBLE (NIST)
• Documentation: COMPREHENSIVE
• Source code: PUBLIC DOMAIN
• Community support: ACTIVE (decades of use)
• Academic citations: >10000
• Active development: Ongoing (v2.0)
• Specialized strength: NIST standard micromagnetic code, public domain, extensive validation, extensible Oxs architecture