Hotspice is the best first choice for thermally active artificial spin ice when each nanomagnet can be represented as a fixed-axis, two-state element. Its kinetic Monte Carlo model includes chronological Néel–Arrhenius switching and Metropolis–Hastings sampling. For an octahedral lattice with built-in monopole and structure-factor analysis, use octahedral-ice. For continuous atomistic spins, finite-temperature dynamics, long-range interactions, or GPU acceleration, start with Spirit; VAMPIRE and UppASD are broader atomistic alternatives. SpinW/pySpinW is the better fit when constructing and analysing a general Hamiltonian, classical Monte Carlo, reciprocal-space correlations, and spin-wave spectra matter more than an artificial-spin-ice-specific workflow.
The important choice is not which project is “fastest.” No authoritative cross-project benchmark establishes that. Choose by the spin representation, lattice, Hamiltonian, dynamics, programming environment, hardware, and observables your study requires.
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Which spin-ice simulator should you choose?
Start by writing down three decisions before installing anything:
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- Physics: thermally activated switching, equilibrium sampling, atomistic spin dynamics, or reciprocal-space analysis?
- Geometry and outputs: square, kagome, pyrochlore, octahedral, or another lattice; and do you need monopole density, structure factors, thermodynamics, or spin-wave spectra?
Those answers lead to a practical shortlist:
| Primary need | Best starting point | Why it fits |
|---|---|---|
| Thermally active artificial spin ice made from elongated, Ising-like islands | Hotspice | Kinetic Monte Carlo with fixed spin axes, binary states, chronological Néel–Arrhenius switching, and Metropolis–Hastings sampling |
| Octahedral or antiperovskite ice models | octahedral-ice | Focused Monte Carlo programs plus scripts for magnetisation, monopole density, structure factors, thermodynamics, and neutron-scattering-style analysis |
| Continuous spins, LLG dynamics, DMI, dipolar terms, or CPU/GPU execution | Spirit | Monte Carlo, Landau–Lifshitz–Gilbert dynamics, Python scripting, OpenMP CPU parallelisation, and CUDA GPU backends |
| General atomistic magnetic materials | VAMPIRE or UppASD | Stochastic or atomistic spin dynamics and several Monte Carlo methods for Heisenberg-style systems |
| Hamiltonian construction, symmetry, correlations, and spin waves | SpinW/pySpinW | Classical Monte Carlo and linear spin-wave workflows in MATLAB and Python projects |
These categories overlap. A project can begin with Hotspice for a rapid artificial-spin-ice prototype and later move to a continuous-spin code when island reversal, intra-island modes, or non-Ising interactions become central. That is a change of model, not a claim that one program reproduces the other automatically.
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Hotspice: the most direct route to thermally active artificial spin ice
Hotspice is described by its project documentation as “a kinetic Monte Carlo simulation package for thermally active artificial spin ices, using an Ising-like approximation.” That description captures its main advantage: it maps naturally onto lithographically patterned nanomagnets whose long axis defines an easy axis and whose relevant state is approximately binary.
What its model represents
Each island has a fixed spin axis and two allowed orientations. Thermal evolution is treated as switching between those states rather than as a continuously rotating magnetic vector. This is often the right abstraction for artificial spin ice when the scientific question concerns vertex populations, ordering, defects, relaxation, or the temperature and time dependence of island reversals.
The package documentation describes two complementary sampling modes:
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- Chronological Néel–Arrhenius switching: transitions are assigned thermally activated rates, allowing events to be ordered in simulated time. This is the relevant mode when relaxation times and thermally driven kinetics are part of the result.
- Metropolis–Hastings sampling: trial changes are accepted according to an equilibrium Monte Carlo rule. This is useful for exploring configurations and thermodynamic distributions when physical event timing is not the primary observable.
The associated publication covers both in-plane and out-of-plane artificial-spin-ice systems, so the package is not limited to one orientation of the patterned array.
When Hotspice is the right abstraction
- Your elements have a strong easy axis and can reasonably be treated as two-state objects.
- You need thermally active behaviour rather than only a zero-temperature energy minimisation.
- You want a Python-oriented workflow for geometry, simulation control, and analysis.
- Your observables are island configurations, vertex populations, relaxation, magnetisation, correlations, or defect statistics.
Where its simplification matters
A fixed-axis Ising-like model does not resolve the detailed magnetisation texture inside an island or the continuous trajectory of a reversal. It therefore cannot, by itself, answer questions about internal domain-wall structure, precessional motion, atomistic damping, or a reversal path that substantially leaves the easy axis. If those effects determine your conclusion, use a continuous-spin or atomistic method instead of treating Hotspice as a substitute for one.
octahedral-ice: a focused code for octahedral and antiperovskite lattices
The octahedral-ice repository targets a narrower geometry than the general-purpose frameworks. It provides Monte Carlo programs for dipolar and near-neighbour models and analysis scripts aimed at the observables commonly used in octahedral-ice studies.
What is included
- Monte Carlo simulations for both dipolar and near-neighbour interaction models.
- Magnetisation calculations.
- Emergent monopole-density measurements.
- Structure-factor calculations.
- Thermodynamic observables.
- Neutron-scattering-style analysis scripts for comparing simulated correlations with reciprocal-space measurements.
This concentration can save substantial model-building work when your lattice and observables match the project. Instead of assembling an octahedral lattice, defining the ice variables, and writing every reciprocal-space diagnostic yourself, you can inspect the supplied programs and adapt the established analysis flow.
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When to choose it over a general framework
Choose octahedral-ice when the lattice is genuinely octahedral or antiperovskite and monopoles, structure factors, and thermodynamics are first-class outputs. A broader framework may still be preferable if you need several unrelated geometries, continuous spins, atomistic dynamics, or extensive custom interactions. The focused code is an efficient scientific starting point, not a universal replacement for Spirit, VAMPIRE, UppASD, or SpinW.
Spirit: the broadest open-source option for continuous spins and dynamics
Spirit is designed as a general spin-simulation framework rather than a spin-ice-only package. Its project documentation says that it runs on most Linux, macOS, and Windows systems and can even run in a browser. More importantly for model selection, it combines Monte Carlo with Landau–Lifshitz–Gilbert (LLG) dynamics and exposes Python scripting.
Capabilities that distinguish Spirit
- Continuous spin vectors: use this when a moment is not adequately represented by two fixed orientations.
- Monte Carlo: sample finite-temperature configurations and equilibrium properties.
- LLG dynamics: study time-dependent magnetisation, precession, damping, and driven evolution.
- Interaction flexibility: model exchange, anisotropy, external fields, dipolar interactions, and other terms supported by the framework.
- Python scripting: automate parameter sweeps, initial states, observables, and post-processing.
- Parallel execution: OpenMP CPU parallelisation and CUDA GPU backends are documented.
For artificial spin ice, Spirit becomes attractive when the islands are only approximately Ising-like, when you need to compare discrete and continuous behaviour, or when the question involves dynamics that a state-to-state kinetic model cannot resolve. It is also a natural choice for a study that begins with an ice geometry but later expands to general magnetic textures.
Trade-offs compared with Hotspice
Spirit’s generality brings more modelling choices and a larger conceptual surface. You must define continuous-spin parameters, dynamics, and numerical settings that a fixed-axis kinetic model hides. That extra work is justified when those degrees of freedom affect the physics; it is unnecessary overhead for a study whose intended model is explicitly binary.
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VAMPIRE
VAMPIRE describes itself as “a high performance general purpose code for the atomistic simulation of magnetic materials.” Its documented methods include stochastic LLG dynamics, Metropolis Monte Carlo, and constrained Monte Carlo. This makes it suitable for atomistic systems in which temperature, dynamic trajectories, constraints, and material-level parameters matter beyond an artificial-spin-ice abstraction.
Use VAMPIRE when your model needs atomistic spins, stochastic dynamics, or constrained sampling and the lattice is part of a broader magnetic-materials problem. It is not presented as a spin-ice-specific package, so you should expect to construct more of the geometry and analysis pipeline yourself than with Hotspice or octahedral-ice.
UppASD
UppASD focuses on atomistic spin dynamics and Monte Carlo for Heisenberg systems. It is a strong candidate when the central object is a general Heisenberg material rather than a canonical artificial-spin-ice array. Its atomistic scope lets you investigate continuous moments and finite-temperature dynamics, while its Monte Carlo functionality supports equilibrium studies.
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Neither VAMPIRE nor UppASD should be selected merely because “atomistic” sounds more accurate. Atomistic resolution increases the number of parameters, the computational workload, and the opportunities for uncertainty in material constants. If a fixed-axis model answers the scientific question, the simpler representation is often easier to validate and interpret.
SpinW and pySpinW: Hamiltonians, correlations, and spin waves
SpinW is oriented toward constructing and analysing general spin Hamiltonians. Its documented features include classical Monte Carlo and linear spin-wave calculations, with MATLAB and Python projects. The framework covers exchange interactions, anisotropy, magnetic fields, crystal symmetry, and correlation functions.
Why it is valuable for spin-ice work
- Build a Hamiltonian with explicit exchange, anisotropy, field, and symmetry terms.
- Use classical Monte Carlo to explore configurations and thermodynamics.
- Calculate correlation functions and reciprocal-space observables.
- Use linear spin-wave calculations when excitations around an ordered state are part of the study.
That combination makes SpinW particularly useful for connecting a model Hamiltonian to scattering or excitation predictions. It is less specialised than octahedral-ice for ready-made monopole workflows and less focused than Hotspice on chronological switching of binary islands.
MATLAB and Python transition considerations
The project repository states that development is moving toward Python and C++, while the MATLAB line is being stabilised around version 3.1.1. Treat that as a compatibility decision: verify the current state of the branch, supported dependencies, and examples before committing to a long-running project. A script written for the stabilised MATLAB line may not transfer directly to an evolving Python/C++ interface, and vice versa.
Comparison by simulation method and physical representation
| Tool | Primary representation | Monte Carlo | Dynamics | Typical strength |
|---|---|---|---|---|
| Hotspice | Fixed-axis, binary Ising-like islands | Metropolis–Hastings; kinetic switching framework | Chronological Néel–Arrhenius thermal events | Thermally active artificial spin ice |
| octahedral-ice | Lattice ice variables for octahedral/antiperovskite models | Near-neighbour and dipolar Monte Carlo | Not the repository’s primary emphasis | Monopoles, structure factors, thermodynamics |
| Spirit | Continuous classical spins | Yes | Landau–Lifshitz–Gilbert | Flexible finite-temperature and dynamic studies |
| VAMPIRE | Atomistic spins | Metropolis and constrained Monte Carlo | Stochastic LLG | General atomistic magnetic materials |
| UppASD | Continuous Heisenberg spins | Yes | Atomistic spin dynamics | Heisenberg-material simulations |
| SpinW/pySpinW | General spin Hamiltonians | Classical Monte Carlo | Linear spin-wave calculations rather than a general LLG engine | Symmetry, correlations, reciprocal space, and excitations |
Interactions, geometry, and observables
Dipolar versus short-range interactions
Dipolar coupling is central to many artificial-spin-ice models because islands interact over long range. Hotspice is intended for artificial-spin-ice systems, while octahedral-ice explicitly supplies both dipolar and near-neighbour programs. Spirit, VAMPIRE, UppASD, and SpinW are broader frameworks in which the supported interaction terms must be configured for the chosen model.
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Do not compare a dipolar simulation with a nearest-neighbour simulation as though they were interchangeable implementations. Long-range terms can alter ordering, defect energetics, structure factors, and finite-size effects. Record the interaction truncation and boundary treatment with every result.
Monopoles and structure factors
If emergent monopole density is a headline result, octahedral-ice offers the most direct supplied analysis path. Other frameworks can be used to compute equivalent observables, but you may need to define the charge convention, identify vertices or tetrahedra, and write the counting and reciprocal-space analysis yourself.
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Structure factors are especially sensitive to lattice definition, system size, reciprocal-space sampling, and whether the simulated observable is being compared with a neutron-scattering-style quantity. Use the same convention across parameter sweeps; otherwise a change in analysis can look like a physical change.
Spin-wave spectra
SpinW/pySpinW is the clearest fit when linear spin-wave calculations and reciprocal-space correlations are central. Monte Carlo alone produces sampled configurations and thermodynamic information; it does not automatically provide a spin-wave spectrum. Conversely, a spin-wave workflow is generally not a replacement for chronological thermal switching in a nanomagnet array.
A practical selection workflow
- Define the lattice. Write down the unit cell, coordination, boundaries, and whether the system is square, kagome, pyrochlore, octahedral, antiperovskite, or another geometry.
- Define the spin variable. Choose fixed-axis binary islands, continuous classical vectors, or atomistic Heisenberg moments. If you cannot justify the choice physically, run a small sensitivity study before scaling up.
- List the Hamiltonian terms. Include exchange, dipolar coupling, anisotropy, external field, Dzyaloshinskii–Moriya interaction where relevant, and any disorder or temperature dependence.
- Specify the time question. For equilibrium distributions, use Monte Carlo. For event timing in binary islands, use a chronological Néel–Arrhenius approach. For continuous precession and damping, use LLG dynamics.
- List required outputs. Decide whether you need magnetisation, vertex populations, monopole density, structure factors, thermodynamic quantities, correlation functions, or spin-wave spectra.
- Match the software. Prototype a thermally active artificial-spin-ice model in Hotspice; inspect octahedral-ice for octahedral observables; evaluate Spirit for continuous spins and GPU execution; compare VAMPIRE and UppASD for general atomistic work; and use SpinW/pySpinW for Hamiltonian and reciprocal-space analysis.
- Reproduce a small reference case. Before a large sweep, check that energies, limiting temperatures, symmetry, and observables behave as expected for a system small enough to inspect.
- Scale only after validation. Increase lattice size, interaction range, trajectory length, or temperature resolution after the baseline case is stable and documented.
How to design a reproducible spin-ice simulation
Record the model, not just the software name
A statement such as “simulated in Spirit” or “run with Hotspice” is not enough to reproduce a result. Record the lattice coordinates, moment magnitude or island parameters, anisotropy, interaction cutoff, boundary conditions, field protocol, temperature schedule, random-seed policy, equilibration period, sampling interval, and observable definitions.
Separate equilibration from measurement
Monte Carlo configurations generated before equilibration can retain the bias of the initial state. For kinetic simulations, the early-time trajectory is itself meaningful only if the initial condition and time origin are part of the experiment. Keep equilibration, production sampling, and transient analysis separate in both files and plots.
Check finite-size and schedule effects
Compare more than one lattice size when ordering or correlations are important. For thermal ramps, test whether changing the ramp or event-sampling schedule changes the apparent transition or defect density. These checks are more informative than an unsupported claim that one package is universally faster.
Performance and hardware: what can and cannot be compared
Spirit documents OpenMP CPU parallelisation and CUDA GPU backends. That gives it a clear hardware option when continuous-spin simulations can benefit from parallel execution. The other projects have different algorithms, data structures, and analysis overheads, so a project label alone does not establish a speed ranking.
No authoritative numerical benchmark or comparative performance percentage is established across these tools. Runtime depends on model size, interaction range, number of neighbours, update algorithm, equilibration length, output frequency, hardware, compiler, and whether the calculation is CPU- or GPU-bound. Report those conditions if you publish timing results, and benchmark the exact model you intend to run.
Common failure modes and how to avoid them
Using an Ising model for a continuous-spin question
If the conclusion depends on reversal pathways, precession, canting, or internal magnetisation, a fixed-axis binary model removes the degrees of freedom you need. Move to Spirit, VAMPIRE, or UppASD and state which continuous-spin parameters were introduced.
Assuming Monte Carlo time is physical time
Metropolis steps provide a sampling procedure, not automatically a calibrated clock. Use Hotspice’s chronological Néel–Arrhenius mode when event timing is the intended physical observable, and distinguish that timeline from an arbitrary Monte Carlo sweep count.
Comparing dipolar and near-neighbour results without labelling them
Long-range dipolar and truncated near-neighbour models can produce different phases and defect populations. Keep the interaction model in plot labels, filenames, and metadata.
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Choosing software before defining observables
A package that can generate configurations may still lack the analysis you need. If monopole density and neutron-scattering-style structure factors are mandatory, start with octahedral-ice or budget time to implement and validate those diagnostics elsewhere. If spin-wave spectra are mandatory, include SpinW/pySpinW in the evaluation even if another code handles the thermal sampling.
Ignoring version and interface changes
Check the current documentation and examples before freezing an environment. SpinW’s documented transition toward Python and C++ alongside a stabilised MATLAB line around 3.1.1 is a specific reminder that language, API, and dependency assumptions can change during a project.
Bottom-line recommendations by research question
- “I need thermally active artificial spin ice with binary islands.” Start with Hotspice.
- “I need octahedral-ice Monte Carlo plus monopoles and structure factors.” Start with octahedral-ice.
- “I need continuous spins, LLG, DMI, dipolar terms, or GPU support.” Evaluate Spirit first.
- “I am modelling a general atomistic Heisenberg material.” Compare VAMPIRE and UppASD against your required dynamics and Monte Carlo method.
- “I need Hamiltonian symmetry, correlations, classical Monte Carlo, and spin waves.” Evaluate SpinW/pySpinW, while checking whether the MATLAB or Python/C++ workflow matches your environment.
The best open-source spin-ice software is therefore conditional: Hotspice is the strongest default for thermally active artificial spin ice, while Spirit is the most versatile general framework. Octahedral-ice wins on focused lattice-specific analysis; VAMPIRE and UppASD serve broader atomistic studies; and SpinW/pySpinW excels at Hamiltonian and reciprocal-space work. Select the representation and observables first, then choose the code that implements them with the fewest unvalidated assumptions.
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