UppASD

Official Resources

• Source Repository: https://github.com/UppASD/UppASD
• GitLab: https://gitlab.com/UppASD/UppASD
• Documentation: https://uppasd.github.io/UppASD/
• License: GNU General Public License v3

Overview

UppASD (Uppsala Atomistic Spin Dynamics) is a simulation tool for atomistic spin dynamics and Monte Carlo simulations of Heisenberg spin systems. It studies magnetization dynamics using the atomistic Landau-Lifshitz-Gilbert (LLG) equation and can compute magnon dispersion via linear spin-wave theory.

Scientific domain: Atomistic spin dynamics, Monte Carlo magnetism, magnon spectroscopy
Target user community: Researchers studying magnetic materials at the atomistic level, magnon dispersion, and phase transitions

Theoretical Methods

• Atomistic Landau-Lifshitz-Gilbert (LLG) equation
• Monte Carlo simulations (Metropolis, heat bath)
• Heisenberg Hamiltonian (isotropic, anisotropic, DM)
• Linear spin-wave theory for magnon dispersion
• Spin-spin correlation functions
• Dzyaloshinskii-Moriya interaction
• Uniaxial and biaxial anisotropy
• External magnetic fields

Capabilities (CRITICAL)

• Atomistic spin dynamics simulation
• Monte Carlo simulation (equilibrium properties)
• Magnon dispersion calculation
• Spin-spin correlation functions
• Critical temperature (Tc) determination
• Magnetic ground state identification
• Temperature-dependent magnetization
• Hysteresis loops
• Non-collinear magnetism
• Spin-lattice coupling (external)

Sources: GitHub repository, O. Eriksson et al., "Atomistic Spin Dynamics" (Oxford University Press, 2017)

Key Strengths

Comprehensive Spin Dynamics:

• LLG equation integration
• Multiple integration schemes
• Stochastic thermal fluctuations
• Time-dependent magnetization

Monte Carlo:

• Equilibrium properties
• Phase transitions
• Critical temperatures
• Ground state determination

Magnon Spectroscopy:

• Linear spin-wave theory
• Magnon dispersion
• Dynamical structure factor
• Comparison with INS/RIXS

DFT Integration:

• Exchange parameters from DFT
• DM vectors from DFT
• Anisotropy from DFT
• Combined DFT+ASD workflow

Inputs & Outputs

• Input formats:

  • UppASD input files (jfile, kfile, etc.)
  • Exchange coupling parameters
  • DM interaction vectors
  • Anisotropy constants

• Output data types:

  • Magnetization vs temperature
  • Magnon dispersion
  • Spin-spin correlations
  • Hysteresis loops
  • Energy vs time

Interfaces & Ecosystem

• DFT codes: Exchange parameter input
• AiiDA: aiida-uppasd plugin available
• Python: Post-processing scripts
• VASP/QE: Parameter extraction workflows

Performance Characteristics

• Speed: Fast (LLG integration)
• Accuracy: Depends on exchange parameters
• System size: Millions of spins
• Parallelization: MPI + OpenMP

Computational Cost

• Equilibrium MC: Hours
• Spin dynamics: Hours
• Magnon dispersion: Minutes
• Typical: Moderate

Limitations & Known Constraints

• Classical spins: No quantum effects
• Heisenberg model: Limited Hamiltonian forms
• Exchange parameters: Need external DFT calculation
• No orbital moments: Spin-only dynamics
• 3D only: No 2D-specific optimizations

Comparison with Other Codes

• vs Spirit: UppASD is Fortran, Spirit is C++
• vs VAMPIRE: UppASD has magnon dispersion, VAMPIRE is more general
• vs SpinW: UppASD is dynamics, SpinW is spin-wave theory
• Unique strength: Atomistic spin dynamics with magnon dispersion, Monte Carlo, and DFT integration

Application Areas

Magnetic Materials:

• Transition metal ferromagnets
• Rare-earth magnets
• Antiferromagnets
• Ferrimagnets

Magnon Spectroscopy:

• Magnon dispersion comparison
• Inelastic neutron scattering
• RIXS magnon spectra
• Spin waves in nanostructures

Phase Transitions:

• Critical temperature calculation
• Order-disorder transitions
• Spin reorientation transitions
• Multiferroic transitions

Spintronics:

• Domain wall dynamics
• Skyrmion dynamics
• Spin torque effects
• Ultrafast demagnetization

Best Practices

Exchange Parameters:

• Use well-converged DFT calculations
• Include sufficient neighbor shells
• Validate against experimental Tc
• Consider DM interaction for non-centrosymmetric

Monte Carlo:

• Use sufficient thermalization steps
• Average over multiple runs
• Test finite-size effects
• Use heat bath for faster convergence

Spin Dynamics:

• Choose appropriate time step
• Include thermal fluctuations
• Monitor energy conservation
• Use sufficient averaging

Community and Support

• Open source (GPL v3)
• Developed at Uppsala University
• Published textbook: "Atomistic Spin Dynamics" (Oxford, 2017)
• Active development
• AiiDA plugin available

Verification & Sources

Primary sources:

1. GitHub: https://github.com/UppASD/UppASD
2. O. Eriksson et al., "Atomistic Spin Dynamics" (Oxford University Press, 2017)
3. A. Bergman et al., Phys. Rev. B 81, 144416 (2010)

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Source code: ACCESSIBLE (GitHub/GitLab)
• Documentation: ACCESSIBLE
• Published methodology: Oxford University Press
• Active development: Ongoing
• Specialized strength: Atomistic spin dynamics with magnon dispersion, Monte Carlo, DFT integration