For any inquiries or assistance, feel free to contact Mr. CAO Bin at: 📧 Email: [email protected] Cao Bin is a PhD candidate at the Hong Kong University of Science and Technology (Guangzhou), under the supervision of Professor Zhang Tong-Yi. His research focuses on AI for science, especially intelligent crystal-structure analysis and discovery. Learn more about his work on his homepage.

Please star this project to support open-source development. For questions or collaboration, contact Dr. Bin Cao ([email protected]).

The Bgolearn project has received support from the Shanghai Artificial Intelligence Open Source Award Project Support Plan (2025) (上海市人工智能开源奖励项目支持计划, 2025,Project).

Bgolearn is a lightweight and extensible Python package for Bayesian global optimization, developed to accelerate materials discovery and design. It provides out-of-the-box support for regression and classification tasks, integrates multiple acquisition strategies, and enables seamless workflows for virtual screening, active learning, and multi-objective optimization.

Official PyPI: pip install Bgolearn Video Tutorial: Watch on BiliBili Jupyter Demo: Run it Online

• Bgolearn
• BgoKit
• MultiBgolearn

pip install Bgolearn

pip install --upgrade Bgolearn

pip show Bgolearn

Detailed tutorials and documentation are available at: https://bgolearn.netlify.app/

If you use Bgolearn in your research, please cite:

@article{cao2026bgolearn,
  title        = {Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery},
  author       = {Cao, Bin and Xiong, Jie and Ma, Jiaxuan and Tian, Yuan and Hu, Yirui and He, Mengwei and Zhang, Longhan and Wang, Jiayu and Hui, Jian and Liu, Li and Xue, Dezhen and Lookman, Turab and Zhang, Tong-Yi},
  journal      = {arXiv preprint arXiv:2601.06820},
  year         = {2026},
  eprint       = {2601.06820},
  archivePrefix= {arXiv},
  primaryClass = {cond-mat.mtrl-sci},
  doi          = {https://doi.org/10.48550/arXiv.2601.06820},
  note         = {38 pages, 5 figures}
}

Released under the MIT License. Free for academic and commercial use. Please cite relevant publications when used in research.

We welcome community contributions and collaborations:

• Submit issues for bug reports, ideas, or feature suggestions
• Submit pull requests for code improvements
• Contact Bin Cao ([email protected]) for research collaboration opportunities