Bayesian optimization structure search
Project description
Bayesian Optimization Structure Search (BOSS) is a general-purpose Bayesian Optimization code. It is designed to facilitate machine learning in computational and experimental natural sciences. See research examples for various applications of BOSS.
For a more detailed description of the code and tutorials, please consult the user guide.
Installation
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 section.
Usage
Tutorials to get you started are available in our user guide. Detailed descriptions of how BOSS operates are available in the manual.
Credits
BOSS is under active development in the Materials Informatics Laboratory at the University of Turku and the Computational Electronic Structure Theory (CEST) group at Aalto University. For the full list of authors see BOSS people.
If you wish to use BOSS in your research, please use the citation.
Issues and feature requests
It is strongly encouraged to submit bug reports, questions, and feature requests via the gitlab issue tracker. The BOSS development team can be contacted by email at milica.todorovic@utu.fi
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