SPIN SIMULATION FRAMEWORK
|Branch||Python Package Coverage||Core Library Coverage|
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
G. P. Müller et al., Phys. Rev. B 99, 224414 (2019) and read 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 firstname.lastname@example.org
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 http://juspin.de
- Getting started with the Desktop User Interface
- Getting started with the Python Package
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++11. It combines the traditional cluster work, using using the command-line, with modern visualisation capabilites 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 used from almost any programming language, and includes ready-to-use python bindings. A powerful desktop user interface is available, providing real-time visualisation and control of parameters.
- Atomistic Spin Lattice Heisenberg Model including also 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)
Getting started with the Desktop Interface ¶
Desktop UI with Isosurfaces in a thin layer
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 Visualization (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 ¶
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)
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)
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!