Metadata-Version: 2.1
Name: MultiBgolearn
Version: 0.0.2
Summary: Python package designed for multi-objective Bayesian global optimization (MOBO)
Home-page: https://github.com/Bin-Cao/MultiBgolearn
Author: CaoBin
Author-email: bcao686@connect.hkust-gz.edu.cn
Maintainer: CaoBin
Maintainer-email: bcao686@connect.hkust-gz.edu.cn
License: MIT License
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.5
Requires-Dist: scikit-learn
Requires-Dist: openpyxl
Requires-Dist: art

MultiBgolearn is a Python package designed for multi-objective Bayesian global optimization (MOBO), specifically tailored for materials design. It extends the functionalities of the Bgolearn package, which focuses on single-objective optimization, by enabling the simultaneous optimization of multiple material properties. This makes MultiBgolearn highly suitable for real-world applications where trade-offs between competing objectives are common.

The repository provides the source code of the MultiBgolearn package along with several multi-objective Bayesian global optimization (MOBO) algorithms.

For questions or suggestions, feel free to contact: Bin Cao: [bcao686@connect.hkust-gz.edu.cn](mailto:bcao686@connect.hkust-gz.edu.cn), GitHub: [https://github.com/Bin-Cao/MultiBgolearn](https://github.com/Bin-Cao/MultiBgolearn)
