Intuition
A piece of iron has electrons. No simulation will ever follow them all. So magnetism is a multiscale subject: each scale keeps only the degrees of freedom that matter at that resolution, and hands the parameters it produces up to the next scale.
Three scales dominate:
- Ab-initio (DFT) — every electron explicit, on the scale of angstroms and femtoseconds.
- Atomistic spin — one classical vector per atom, on the scale of nanometers and picoseconds.
- Micromagnetics — a continuous field , on the scale of – nm and nanoseconds to microseconds.
Each scale is the coarse-graining of the previous one. The boundary between them is set by the exchange length – nm: below it, the spin field doesn’t really vary, so a continuum is fine; above it, even atomistic models become wasteful.
Formal hierarchy
| Scale | Method | Length | Time | DoF | What it computes |
|---|---|---|---|---|---|
| Quantum | DFT | nm | fs | electrons | atomic moments, , , exchange integrals |
| Atomic | atomistic spin | nm | ps | one per atom | thermodynamics at , ultrafast switching |
| Continuum | micromagnetics | – nm | ns–µs | domain walls, vortices, devices, hysteresis |
Each upper level inherits parameters from the level below: DFT gives the exchange stiffness , anisotropy , and atomic moment that the atomistic spin model uses; the atomistic model in turn provides the saturation magnetization and damping used by LLG micromagnetic simulations.
Key results
1. Why a continuum is allowed at all
The continuum approximation rests on a single observation: in a ferromagnet, exchange enforces parallel alignment over a length . On scales much shorter than that, the spin field is essentially uniform — there is nothing for a finer description to resolve. On scales much longer, atomic granularity disappears entirely. The “useful” window where varies smoothly but appreciably is exactly the regime where micromagnetics works.
This is the same physics that makes the Navier–Stokes equation a useful description of water even though water is made of molecules: the molecular mean free path is the analogue of .
2. Where each scale wins
- DFT is essential when the atomic moment itself matters — determining whether an interface is ferro- or antiferromagnetic, computing of an interface, predicting tunneling spin polarizations.
- Atomistic spin is the right tool for finite-temperature thermodynamics (, critical exponents) and for ultrafast laser-driven demagnetization (sub-picosecond dynamics where the continuum picture is suspect).
- Micromagnetics is the workhorse for devices: hard-disk read heads, MRAM cells, spin-torque oscillators, domain-wall race-track memories. Tools: OOMMF, MuMax3, MagPar, Fidimag.
3. Multiscale handoff
The standard recipe to design a new spintronic stack is:
- DFT the stack — get , , , interfacial , .
- Feed those parameters into a micromagnetic simulation of the device geometry, with the LLG-Slonczewski equation if currents matter.
- Compare to measured switching curves, FMR spectra, R(H) loops.
- Iterate by adjusting the stack composition.
The big computational bottleneck is step 2: the magnetostatic field is nonlocal and dominates simulation cost. State-of-the-art codes accelerate it with FFT, FMM, or tensor methods on GPUs.
Summary
Magnetism is a three-scale subject:
- DFT (electrons, fs, 0.1 nm) — what an atom’s moment is and how it talks to its neighbors.
- Atomistic spin (one per atom, ps, nm) — finite- thermodynamics, ultrafast switching.
- Micromagnetics (, ns, – nm) — domain walls, vortices, devices.
The boundary that sets the continuum approximation is the exchange length – nm. The rest of this wiki lives at the third scale.
Connections
- micromagnetic-energy — the continuum energy functional itself
- llg-equation — the dynamics equation solved at the micromagnetic scale
- magnetic-domains — sub-µm structures naturally captured by micromagnetics
- stoner-model — itinerant-electron picture that lives at the DFT scale
References
- A. Aharoni, Introduction to the Theory of Ferromagnetism (Oxford, 2000), Ch. 9.
- R. F. L. Evans et al., Atomistic Spin Model Simulations of Magnetic Nanomaterials, J. Phys. Condens. Matter 26, 103202 (2014).
- A. Vansteenkiste et al., The design and verification of MuMax3, AIP Adv. 4, 107133 (2014).