Bgolearn

A Unified Bayesian Optimization Framework for Accelerating Materials Discovery

---

About

Bgolearn is a research-oriented Python framework for Bayesian Global Optimization (BGO), developed to accelerate data-driven materials discovery and scientific design.

The framework provides:

• Unified regression and classification modeling
• Modular acquisition functions
• Multi-objective optimization
• Active learning workflows
• Virtual screening pipelines

Bgolearn emphasizes reproducibility, extensibility, and research-grade rigor, making it suitable for both academic research and industrial applications.

---

Paper and Resources

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

• Paper: https://doi.org/10.48550/arXiv.2601.06820
• Conference Report: https://cmc2025.scimeeting.cn/cn/web/speaker-detail/27167
• Documentation: https://bgolearn.netlify.app/
• 中文手册: https://bgolearn-chi.netlify.app/
• Video Tutorial: https://www.bilibili.com/video/BV1LTtLeaEZp

---

Framework

---

Installation

Install from PyPI:

pip install Bgolearn

Upgrade to the latest version:

pip install --upgrade Bgolearn

Check installed version:

pip show Bgolearn

---

Citation

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}
}

---

Funding

Bgolearn is selected for the Open-Source Artificial Intelligence Support Program (2025) by the Shanghai Municipal Commission of Economy and Informatization (上海市经信委).

Project material: https://github.com/Bin-Cao/Bgolearn/blob/main/figures/funding.png

---

Contact

Bin Cao PhD Candidate Hong Kong University of Science and Technology (Guangzhou) Supervisor: Prof. Zhang Tong-Yi Email: bcao686@connect.hkust-gz.edu.cn Homepage: https://bin-cao.github.io/

---

License

Released under the MIT License. Free for academic and commercial use.