The cmtj
name may be misleading -- the MTJ (Magnetic Tunnel Junctions) are not the only structures that may be simulated.
The library allows for macromagnetic simulation of various multilayer spintronic structures. The package uses C++ implementation of (s)LLGS (stochastic Landau-Lifschitz-Gilbert-Slonczewski) equation with various field contributions included for instance: anisotropy, interlayer exchange coupling, demagnetisation, dipole fields etc.
It is also possible to connect devices in parallel or in series to have electrically coupled arrays.
Check out the streamlit hosted demo here. You can simulate:
- PIMM spectra and Spin-Diode spectra
- Try some optimization fitting
- Fit multi-domain or multi-level M(H) or R(H) loops in Domain mode
Let us know if you have any issues with the demo.
The recommended way is to use the pip
package manager and virtualenv (or conda).
Installation is as easy as doing:
- With
virtualenv
(recommended):
$(bash) python -m venv .my-venv
$(bash) source .my-venv/bin/activate
$(.my-venv) python -m pip install cmtj
- Straight from
pip
:
python3 -m pip install cmtj
- Straight from source:
python3 -m pip install git+https://github.com/LemurPwned/cmtj.git
- Clone the repository:
git clone https://github.com/LemurPwned/cmtj.git
python3 -m pip install .
The package requires (if utils
subpackage is used):
- numpy
- scipy
- matplotlib
Documentation: https://lemurpwned.github.io/cmtj. There are many examples available, check out the examples section in the docs
There's a GUI version available! If you wish to conduct a subset of simulations, mainly for experimental modelling, please see the PyMag project. It uses CMTJ as a backend for fast computation.
We would appreciate citing either of the listed work if you decide to use the project or using the cite button on the right hand side panel of the repository:
cmtj: Simulation package for analysis of multilayer spintronic devices
@article{mojsiejuk_cmtj_2023,
title = {cmtj: Simulation package for analysis of multilayer spintronic devices},
volume = {9},
issn = {2057-3960},
url = {https://www.nature.com/articles/s41524-023-01002-x},
doi = {10.1038/s41524-023-01002-x},
pages = {54},
number = {1},
journaltitle = {npj Comput Mater},
author = {Mojsiejuk, Jakub and Ziętek, Sławomir and Grochot, Krzysztof and Skowroński, Witold and Stobiecki, Tomasz},
date = {2023-04-06},
}
Many thanks to professor Jack Sankey for his help with the development of thermal contributions, with inspiration from the macrospinmob project.
All contributions are welcome, please leave an issue if you've encountered any trouble with setup or running the library.
In the docker
directory there's a Dockerfile
that can be used to build a docker image with the library installed.
Dockerfile.app
is used for streamlit development.
There's a .pre-commit-config.yaml
that does some basic python and cpp lints and checks. More static analysis to come in the future.
This may be run with:
pre-commit run -v
or
pre-commit run -a (or --files core/* cmtj/*)
There are couple of stages to building the documentation
- Build Doxygen documentation
This is mostly for the C++ documentation. Furture changes may couple C++ and Python docs.
doxygen Doxyfile
- Build stubs
The stubgen is
pybind11-stubgen
ormypy stubgen
with the latter being preferred now. Before running the stubgen, make sure to install the package with:avoid usingpython3 -m pip install .
-e
flag as it may cause issues with the stubgen. Then to generate, for instance,Stack
module stubs we can do:orstubgen -m cmtj.stack -o target-stub-dir/
More info here: https://mypy.readthedocs.io/en/stable/stubgen.html.python3 -c "import mypy.stubgen; mypy.stubgen.main(['-p', 'cmtj.stack', '-o', 'target-stub-dir/'])"
- Parse stubs to Markdown.
This stage is done by running:
python3 docs/docgen.py
The deployment of the documentation is done via:But first, worth a check:mkdocs gh-deploy
mkdocs serve