Bayesian Optimization Structure Search (BOSS) <https://sites.utu.fi/boss/>_ is a general-purpose Bayesian Optimization code. It is designed to facilitate machine learning in computational and experimental natural sciences. See research examples <https://sites.utu.fi/boss/research/>_ for various applications of BOSS.
For a more detailed description of the code and tutorials, please consult the user guide <https://cest-group.gitlab.io/boss>_.
BOSS is distributed as a PyPI package and can be installed using pip::
python3 -m pip install aalto-boss
We recommend installing BOSS inside a virtual environment (venv, conda...). If you are not using virtual environments, we recommend performing a user-installation instead::
python3 -m pip install --user aalto-boss
Further instructions are provided in the user guide installation <https://cest-group.gitlab.io/boss/installation.html>_ section.
Tutorials to get you started are available in our user guide <https://cest-group.gitlab.io/boss/tutorials.html>. Detailed descriptions of how BOSS operates are available in the manual <https://cest-group.gitlab.io/boss/manual.html>.
BOSS is under active development in the Materials Informatics Laboratory at the University of Turku and the Computational Electronic Structure Theory (CEST) group <http://cest.aalto.fi/>_ at Aalto University. For the full list of authors see BOSS people <https://sites.utu.fi/boss/people/>_.
If you wish to use BOSS in your research, please use the citation <https://sites.utu.fi/boss/about/>_.
It is strongly encouraged to submit bug reports, questions, and feature requests via the
gitlab issue tracker <https://gitlab.com/cest-group/boss/issues>_.
The BOSS development team can be contacted by email at milica.todorovic@utu.fi
FAQs
Bayesian optimization structure search
We found that aalto-boss demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
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