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For thermally active artificial spin ice, start with Hotspice. It is built around an Ising-like approximation for fixed-axis, binary nanomagnets and supports both chronological Néel–Arrhenius switching and Metropolis–Hastings sampling. Choose octahedral-ice when you need a focused Monte Carlo implementation for octahedral or antiperovskite lattices. For continuous atomistic spins, finite-temperature dynamics, long-range interactions, or GPU acceleration, Spirit is the broadest open-source starting point. VAMPIRE and UppASD are general atomistic alternatives, while SpinW/pySpinW is particularly useful for building and analysing Hamiltonians, classical Monte Carlo, and spin-wave spectra.
No single package is “best” for every spin-ice problem. The decisive choices are the lattice, whether each moment is binary or continuous, the interaction terms, the required dynamics, the programming environment, and the observables you need to extract.
What spin-ice software has to represent
Spin ice is not one fixed numerical model. In artificial spin ice, elongated nanomagnets often behave as macrospins constrained to a small number of axes, commonly with two allowed directions along each element. In atomistic or molecular models, every site can instead carry a continuous three-dimensional vector. A code that is excellent for one representation can be awkward or physically inappropriate for the other.
Before comparing packages, specify five things:
- Geometry: square, kagome, pyrochlore, octahedral, antiperovskite, or a custom lattice.
- State space: fixed-axis binary variables, planar vectors, or fully continuous Heisenberg spins.
- Hamiltonian: nearest-neighbour or long-range dipolar coupling, exchange, anisotropy, Dzyaloshinskii–Moriya interaction, external field, or a combination.
- Dynamics: equilibrium Monte Carlo, event-driven thermal switching, deterministic or stochastic Landau–Lifshitz–Gilbert (LLG) dynamics, or a mixture.
- Observables: energy and magnetisation, monopole density, structure factors, thermodynamic curves, real-space correlations, or spin-wave spectra.
Writing these requirements down prevents a common mistake: choosing a package because it has “Monte Carlo” in its feature list even though its spin variables, transition rates, or lattice assumptions do not match the experiment.
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Best open-source option by use case
| Use case | Best starting point | Why it fits | Important qualification |
|---|---|---|---|
| Thermally active artificial spin ice with fixed axes | Hotspice | Ising-like binary elements, kinetic Monte Carlo, chronological Néel–Arrhenius switching, and Metropolis–Hastings sampling | It is an approximation based on fixed spin axes rather than a fully continuous atomistic magnetisation field. |
| Octahedral or antiperovskite ice | octahedral-ice | Focused Monte Carlo programs for dipolar and near-neighbour models, with monopole and structure-factor analysis scripts | Its value is greatest when your geometry and observables match its lattice-specific workflow. |
| Continuous spins, LLG dynamics, and GPU computing | Spirit | Monte Carlo, LLG dynamics, Python scripting, OpenMP CPU parallelisation, and CUDA GPU backends | Model construction is more general, so you must define and validate the lattice and Hamiltonian yourself. |
| General atomistic magnetic materials | VAMPIRE or UppASD | Stochastic or constrained dynamics and Monte Carlo for broad Heisenberg-style systems | Neither is primarily a spin-ice-specific workflow. |
| Hamiltonian construction, reciprocal-space analysis, and spin waves | SpinW/pySpinW | Exchange, anisotropy, fields, crystal symmetry, classical Monte Carlo, correlation functions, and linear spin-wave calculations | The MATLAB and Python/C++ lines are in transition; verify compatibility before committing to a long project. |
There is no authoritative speed ranking among these tools. Runtime depends on system size, interaction range, update algorithm, hardware, and implementation details, so a performance percentage quoted without those conditions would be misleading.
Hotspice: the most direct route for thermally active artificial spin ice
What it models
Hotspice is described as a kinetic Monte Carlo package for thermally active artificial spin ices using an Ising-like approximation. Each elongated element has a fixed axis and a binary state. That abstraction is often the right level for nanomagnet arrays where the key question is how moments switch between two metastable directions rather than how the magnetisation varies continuously inside each element.
The associated documentation and paper cover both in-plane and out-of-plane artificial-spin-ice systems. You can therefore use the same conceptual workflow for planar arrays and geometries in which the easy axes point out of the sample plane, provided the chosen lattice and interaction parameters are represented correctly.
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Its two useful sampling modes
- Chronological Néel–Arrhenius switching: transition rates are tied to thermally activated barriers, so events are selected in physical time order. This is the relevant mode when you want switching trajectories, relaxation times, or temperature-dependent ageing.
- Metropolis–Hastings sampling: trial flips are accepted according to an equilibrium sampling rule. It is useful for exploring thermodynamic states when a physical clock is not the primary result.
These modes answer different questions. A configuration obtained after many accepted Metropolis updates should not automatically be interpreted as a trajectory that an experimental array would follow in real time. Conversely, a kinetic simulation requires credible energy barriers, attempt frequencies, and temperature parameters; otherwise the event times have no defensible physical meaning.
When Hotspice is the right first prototype
- Represent each nanomagnet as a binary variable aligned with its easy axis.
- Define the lattice coordinates and each element’s orientation.
- Choose the interaction model and material parameters, including any long-range dipolar contribution supported by your setup.
- Select chronological switching for physical relaxation or Metropolis–Hastings for equilibrium sampling.
- Run a small array first and compare energy, magnetisation, and defect statistics against hand calculations or a second implementation.
Hotspice is less suitable when the central physics involves domain-wall motion, continuous canting, strongly non-Ising states, or detailed spin-wave dynamics within each element. In those cases, move to a continuous-spin framework rather than adding increasingly elaborate corrections to a binary model.
octahedral-ice: a focused lattice package
What the repository provides
The octahedral-ice repository contains Monte Carlo programs for both dipolar and near-neighbour models on octahedral and related antiperovskite lattices. It also includes analysis scripts for magnetisation, emergent monopole density, structure factors, thermodynamics, and neutron-scattering-style calculations.
That combination matters because lattice-specific observables are often the hardest part to reproduce consistently. A general simulator may generate configurations, but you would then have to implement the vertex classification, monopole counting, reciprocal-space transforms, and thermodynamic aggregation yourself. octahedral-ice gives those analyses a common context.
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When to choose it
- Your lattice is octahedral or an antiperovskite structure rather than a generic square or kagome array.
- You need emergent monopole density as a primary result.
- You want structure factors or neutron-scattering-style outputs alongside real-space thermodynamics.
- A classical Monte Carlo workflow is sufficient and continuous LLG trajectories are not required.
If your geometry is substantially different, inspect how coordinates, boundary conditions, and neighbour lists are encoded before adapting the code. A program can run successfully while silently using the wrong coordination number or vertex convention, producing attractive but irrelevant plots.
Validation checklist for lattice-specific simulations
- Count sites and bonds for a very small periodic cell by hand.
- Confirm that the code’s definition of a monopole matches your charge convention.
- Check that the dipolar cutoff or summation method is stated and held fixed between runs.
- Reproduce a known high-temperature limit, where magnetisation and correlations should be weak.
- Verify that the reciprocal-space grid and form-factor assumptions match the quantity you intend to compare with scattering data.
Spirit: the broad continuous-spin framework
Core capabilities
Spirit is an open-source atomistic spin simulation framework designed for continuous magnetic moments. Its documented methods include Monte Carlo and Landau–Lifshitz–Gilbert dynamics, with Python scripting and operation across Linux, macOS, and Windows; the project also provides browser-based access. OpenMP enables CPU parallelisation, and CUDA backends support compatible GPUs.
This makes Spirit a strong choice when the same project needs both equilibrium sampling and real-time or damped magnetisation dynamics. You can use Monte Carlo to explore finite-temperature states, then use LLG dynamics to examine how a selected state evolves under fields, damping, or other time-dependent conditions.
What Spirit adds beyond an Ising package
- Continuous orientation: spins can cant away from easy axes instead of being restricted to two values.
- Dynamic trajectories: LLG integration exposes precession, damping, and field-driven evolution that a flip-based Monte Carlo history does not represent.
- Hardware choices: OpenMP supports multicore CPU execution, while CUDA can move suitable workloads to a GPU.
- Scriptable studies: Python bindings make parameter sweeps, automated initialisation, and post-processing easier to reproduce.
Trade-offs
Generality shifts more responsibility to you. You must construct the lattice, specify anisotropy directions, select exchange and dipolar terms, set damping and time-step parameters for LLG, and establish that the discretisation corresponds to the physical object. A GPU backend does not guarantee faster execution for every problem: small systems, frequent host-device transfers, or algorithms with poor parallel occupancy may remain CPU-friendly.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Use Spirit when the physics requires continuous vectors, DMI or other non-Ising interactions, coupled Monte Carlo and LLG workflows, or scalable CPU/GPU execution. If all elements are demonstrably binary and your main output is thermally activated switching, Hotspice usually offers a simpler and more transparent model.
VAMPIRE and UppASD: broader atomistic alternatives
VAMPIRE
VAMPIRE describes itself as a high-performance general-purpose code for atomistic simulation of magnetic materials. Its documented methods include stochastic LLG, Metropolis Monte Carlo, and constrained Monte Carlo. Those options cover both dynamical and equilibrium studies and are useful when a spin-ice project is part of a larger investigation of magnetic materials, temperature-dependent behaviour, or nonstandard Hamiltonians.
The cost of that breadth is that spin-ice-specific concepts are not necessarily built into the workflow. You may need to implement lattice generation, vertex classification, monopole identification, and structure-factor analysis externally. Treat the simulator as the engine for spin and energy evolution, then keep analysis scripts versioned separately so definitions remain explicit.
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UppASD
UppASD focuses on atomistic spin dynamics and Monte Carlo for Heisenberg systems. It is a sensible candidate when continuous classical spins and material-level interactions matter more than a prepackaged artificial-spin-ice abstraction. It can occupy the same decision category as VAMPIRE: broad atomistic modelling rather than a dedicated ice-lattice toolkit.
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| Question | Implication |
|---|---|
| Do you need stochastic LLG and several Monte Carlo variants in one documented code? | VAMPIRE is a natural candidate to evaluate. |
| Is your model explicitly a Heisenberg atomistic system? | UppASD belongs on the shortlist. |
| Are monopoles, ice-rule violations, or lattice structure factors the headline outputs? | Start with octahedral-ice or plan a substantial analysis layer around the general code. |
| Do you require Python-driven orchestration and CUDA support? | Evaluate Spirit first, then confirm whether the other code’s interfaces and acceleration options meet the project requirements. |
SpinW and pySpinW: Hamiltonians, Monte Carlo, and spin waves
What they are good at
SpinW provides tools for constructing and analysing general spin Hamiltonians. Its documented features cover exchange interactions, anisotropy, magnetic fields, crystal symmetry, correlation functions, classical Monte Carlo, and linear spin-wave calculations. The ecosystem includes MATLAB and Python projects, making it attractive when reciprocal-space structure, dispersion, or symmetry analysis is as important as real-space configurations.
For a spin-ice study, SpinW can help you express a model, generate classical states, sample with Monte Carlo, and calculate correlations or spin-wave observables. It is particularly useful when the eventual comparison is to scattering data or when you need to relate a real-space Hamiltonian to reciprocal-space spectra.
Version and workflow qualification
The project documentation says development is moving toward Python and C++, while the MATLAB line is being stabilised around version 3.1.1. That transition means a script written for one branch may not transfer directly to another. Confirm the current installation instructions, supported language bindings, and example compatibility before starting a large parameter sweep or building a publication pipeline.
SpinW is not the first choice for a turnkey thermally activated artificial-spin-ice simulation. It becomes more compelling when Hamiltonian flexibility, crystal symmetry, correlation functions, and spin-wave calculations outweigh the convenience of a dedicated kinetic Ising-like package.
Algorithm and physics comparison
| Capability | Hotspice | octahedral-ice | Spirit | VAMPIRE / UppASD | SpinW / pySpinW |
|---|---|---|---|---|---|
| Primary state representation | Fixed-axis binary, Ising-like elements | Lattice-model spins for octahedral or antiperovskite ice | Continuous atomistic spins | Continuous atomistic or Heisenberg-style spins | General classical spin Hamiltonians |
| Monte Carlo | Kinetic Néel–Arrhenius and Metropolis–Hastings | Dipolar and near-neighbour Monte Carlo | Monte Carlo plus LLG | Metropolis, constrained MC, and/or MC depending on code | Classical Monte Carlo |
| Real-time dynamics | Event chronology rather than continuous LLG | Not the primary documented focus | Landau–Lifshitz–Gilbert dynamics | Stochastic LLG and atomistic dynamics | Not the primary documented focus |
| Interaction scope | Ising-like energy model appropriate to the configured artificial ice | Dipolar and near-neighbour lattice models | Exchange, anisotropy, fields, dipolar and other configured terms | General atomistic interactions supported by each code | Exchange, anisotropy, field, symmetry-defined Hamiltonians |
| Languages and acceleration | Python-oriented package | Repository scripts and Monte Carlo programs | Python, OpenMP CPU, CUDA GPU, cross-platform | Code-specific atomistic workflows | MATLAB and Python projects; Python/C++ transition |
| Specialised analysis | Thermal switching and artificial-ice configurations | Monopoles, structure factors, thermodynamics, neutron-scattering-style analysis | General simulation outputs and scripted analysis | General atomistic observables | Correlation functions and spin-wave spectra |
The table describes documented scope, not a universal ranking. “Supports dipolar interactions,” for example, does not tell you whether a particular long-range summation method, boundary condition, or cutoff is appropriate for your system.
A practical selection workflow
Step 1: write the minimum physical model
List the lattice vectors, number of sites, allowed spin states, coupling constants, anisotropy axes, field direction, temperature range, and boundary conditions. Mark each term as essential or optional. This prevents a package’s default settings from defining your physics accidentally.
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Step 2: choose the state space
- Select Hotspice if every element is adequately represented by a fixed axis and two states.
- Select Spirit, VAMPIRE, or UppASD if canting, continuous rotation, atomistic disorder, or dynamical precession is important.
- Use SpinW/pySpinW when the Hamiltonian and reciprocal-space analysis are central.
- Use octahedral-ice when the octahedral or antiperovskite lattice and its established observables are the focus.
Step 3: match the time question to the algorithm
For equilibrium properties, use a suitably equilibrated Monte Carlo chain and report the update rule. For physical switching times, use a kinetic method with measured or justified barriers and attempt frequencies. For field-driven trajectories, use LLG and report damping, time step, and integration settings. Do not compare Monte Carlo step counts directly with nanoseconds.
Step 4: prototype on a solvable system
Start with a small cell whose energy can be enumerated or checked by hand. Test one interaction at a time, then add long-range dipolar terms, fields, and disorder. Record random seeds, software revision, parameter files, and analysis scripts so a changed default cannot silently alter the result.
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Step 5: scale only after validation
Once energies, symmetry, defect counts, and limiting-temperature behaviour agree with expectations, increase system size and sampling length. On Spirit, benchmark your own workload on OpenMP and CUDA rather than assuming the GPU is faster. For repository-specific programs, profile the actual lattice and interaction range you intend to publish.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Installation and reproducibility practices
Open-source availability does not remove environment risks. Keep the simulator and analysis code in separate, versioned environments. Record the operating system, compiler or interpreter version, numerical libraries, GPU driver where relevant, and the exact project revision. A Python script can produce different results after a dependency update if random-number generators, array operations, or default tolerances change.
Use a parameter file rather than editing constants throughout source code. Include:
- lattice dimensions and boundary conditions;
- spin length and orientation conventions;
- exchange, dipolar, anisotropy, and field values with units;
- temperature schedule and equilibration length;
- Monte Carlo move definition or LLG integration settings;
- random seed and output format;
- analysis definitions for charge, structure factor, and correlation functions.
For every production run, retain a short machine-readable manifest linking those parameters to the generated configuration and plots. This is especially important when comparing a MATLAB workflow with a Python workflow or when moving from CPU to GPU execution.
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Common failure modes and fixes
The simulation violates the expected ice rule everywhere
Check the vertex convention, sign of each local axis, bond list, and energy units before changing the temperature. A reversed axis can turn a low-energy ice-rule state into an apparent high-energy defect state without causing a program error.
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Monopole density changes when only the plotting script changes
Verify that both scripts use the same vertex charge definition, boundary treatment, and treatment of missing neighbours. For octahedral-ice workflows, keep the repository’s analysis conventions with the corresponding simulation revision.
Results depend strongly on the initial configuration
Increase equilibration, use independent seeds, and inspect observables as a function of Monte Carlo time. Near ordering or freezing transitions, one chain may be insufficient; compare multiple starting states and report uncertainty from independent runs.
Kinetic switching times look unrealistic
Check the energy barrier, attempt frequency, temperature units, and whether the Ising-like approximation remains valid for the element dimensions. Metropolis–Hastings acceptance counts are not physical time, so they cannot substitute for a calibrated Néel–Arrhenius model.
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For Spirit, VAMPIRE, or UppASD, inspect the LLG time step, damping, spin length, field magnitude, and integration tolerances. Reduce the time step and test a single spin in a known field before running a large lattice.
GPU execution is slower than CPU execution
Measure data-transfer time, occupancy, and the fraction of work that remains serial. Small arrays, frequent output, and long-range interactions with irregular memory access can erase the advantage of CUDA. Keep the CPU result as a reference rather than assuming a hardware change improves numerical quality.
SpinW scripts fail after switching language branches
Check whether the example targets the stabilised MATLAB line around 3.1.1 or the newer Python/C++ direction. Reconfirm object names, import paths, and supported features before porting a complete analysis.
How to make results comparable across packages
Cross-package comparison is meaningful only after aligning the physical model. Use the same lattice coordinates, spin length, interaction constants, boundary conditions, temperature points, and random-seed policy where possible. Export configurations in a neutral format and compute magnetisation, energy, and correlation functions with one independent analysis script. This separates differences in the simulator from differences in post-processing.
Do not compare a binary Hotspice trajectory directly with a continuous Spirit trajectory without defining an observable that both models share. For example, project continuous moments onto each element’s easy axis before comparing binary occupancy or vertex charge. Likewise, compare physical time only between algorithms with calibrated rates; compare Monte Carlo sweeps only as an algorithmic measure, not as elapsed experimental time.
Bottom line for choosing a package
Choose Hotspice for the shortest path to thermally active artificial spin ice with fixed-axis binary elements. Choose octahedral-ice for octahedral or antiperovskite lattices where monopole density, structure factors, and thermodynamics are central outputs. Choose Spirit when continuous spins, LLG dynamics, Python automation, or OpenMP/CUDA execution are requirements. Evaluate VAMPIRE and UppASD for broader atomistic Heisenberg-material studies. Choose SpinW/pySpinW when Hamiltonian construction, symmetry, correlations, classical Monte Carlo, and spin-wave analysis define the project. The best final choice is the one whose state representation and time algorithm match the physical question before any performance or convenience comparison is made.
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