SPIRIT

SPIN SIMULATION FRAMEWORK

Core Library:

Branch	Build Status	Python Package Coverage	Core Library Coverage
master:
develop:

The code is released under MIT License. If you intend to present and/or publish scientific results or visualisations for which you used Spirit, please cite both the spirit paper G. P. Müller et al., Phys. Rev. B 99, 224414 (2019) and the (version-independent) Zenodo entry DOI: 10.5281/zenodo.7746551. For details, please refer to the docs/REFERENCE.md.

This is an open project and contributions and collaborations are always welcome!! See docs/CONTRIBUTING.md on how to contribute or write an email to moritz.sallermann@rwth-aachen.de or t.puerling@fz-juelich.de For contributions and affiliations, see docs/CONTRIBUTORS.md.

Please note that a version of the Spirit Web interface is hosted by the Research Centre Jülich at https://juspin.de

Contents

1. Introduction
2. Getting started with the Desktop User Interface
3. Getting started with the Python Package

Introduction

A modern framework for magnetism science on clusters, desktops & laptops and even your Phone

Spirit is a platform-independent framework for spin dynamics, written in C++17. It combines the traditional cluster work, using the command-line, with modern visualisation capabilities in order to maximize scientists' productivity.

"It is unworthy of excellent men to lose hours like slaves in the labour of calculation which could safely be relegated to anyone else if machines were used."

• Gottfried Wilhelm Leibniz

Our goal is to build such machines. The core library of the Spirit framework provides an easy to use API, which can be embedded into almost any programming language, and includes ready-to-use python bindings. A powerful desktop user interface is available, providing real-time visualisation and control over system parameters.

Physics Features

• Atomistic Spin Lattice Heisenberg Model including among others DMI and dipole-dipole
• Spin Dynamics simulations obeying the Landau-Lifschitz-Gilbert equation
• Direct Energy minimisation with different solvers
• Minimum Energy Path calculations for transitions between different spin configurations, using the GNEB method

Highlights of the Framework

• Cross-platform: everything can be built and run on Linux, OSX and Windows
• Standalone core library with C API which can be used from almost any programming language
• Python package making complex simulation workflows easy
• Desktop UI with powerful, live 3D visualisations and direct control of most system parameters
• Modular backends including parallelisation on GPU (CUDA) and CPU (OpenMP and STL parallelisation)

Documentation

More details may be found at spirit-docs.readthedocs.io or in the Reference section including

• Unix/OSX build instructions
• Windows build instructions
• Input File Reference

There is also a Wiki, hosted by the Research Centre Jülich.

Getting started with the Desktop Interface

See the build instructions for Unix/OSX or Windows on how to get the desktop user interface.

The user interface provides a powerful OpenGL visualisation window using the VFRendering library. It provides functionality to

• Control Calculations
• Locally insert Configurations (homogeneous, skyrmions, spin spiral, …)
• Generate homogeneous Transition Paths
• Change parameters of the Hamiltonian
• Change parameters of the Method and Solver
• Configure the visualisation (arrows, isosurfaces, lighting, …)

See the UI-QT Reference for the key bindings of the various features.

Unfortunately, distribution of binaries for the Desktop UI is not possible due to the restrictive license on QT-Charts.

Getting started with the Python Package

To install the Spirit python package, either build and install from source (Unix/OSX, Windows) or simply use

pip install spirit

With this package you have access to powerful Python APIs to run and control dynamics simulations or optimizations. This is especially useful for work on clusters, where you can now script your workflow, never having to re-compile when testing, debugging or adding features.

The most simple example of a spin dynamics simulation would be

from spirit import state, simulation
with state.State("input/input.cfg") as p_state:
    simulation.start(p_state, simulation.METHOD_LLG, simulation.SOLVER_SIB)

Where SOLVER_SIB denotes the semi-implicit method B and the starting configuration will be random.

To add some meaningful content, we can change the initial configuration by inserting a Skyrmion into a homogeneous background:

def skyrmion_on_homogeneous(p_state):
    from spirit import configuration
    configuration.plus_z(p_state)
    configuration.skyrmion(p_state, 5.0, phase=-90.0)

If we want to calculate a minimum energy path for a transition, we need to generate a sensible initial guess for the path and use the GNEB method. Let us consider the collapse of a skyrmion to the homogeneous state:

from spirit import state, chain, configuration, transition, simulation

### Copy the system and set chain length
chain.image_to_clipboard(p_state)
noi = 7
chain.set_length(p_state, noi)

### First image is homogeneous with a Skyrmion in the center
configuration.plus_z(p_state, idx_image=0)
configuration.skyrmion(p_state, 5.0, phase=-90.0, idx_image=0)
simulation.start(p_state, simulation.METHOD_LLG, simulation.SOLVER_VP, idx_image=0)
### Last image is homogeneous
configuration.plus_z(p_state, idx_image=noi-1)
simulation.start(p_state, simulation.METHOD_LLG, simulation.SOLVER_VP, idx_image=noi-1)

### Create transition of images between first and last
transition.homogeneous(p_state, 0, noi-1)

### GNEB calculation
simulation.start(p_state, simulation.METHOD_GNEB, simulation.SOLVER_VP)

where SOLVER_VP denotes a direct minimization with the velocity projection algorithm.

You may also use Spirit order to extract quantitative data, such as the energy.

def evaluate(p_state):
    from spirit import system, quantities
    M = quantities.get_magnetization(p_state)
    E = system.get_energy(p_state)
    return M, E

Obviously you may easily create significantly more complex workflows and use Python to e.g., pre- or post-process data or to distribute your work on a cluster and much more!