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For any inquiries or assistance, feel free to contact Mr. CAO Bin at: 📧 Email: bcao686@connect.hkust-gz.edu.cn 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. |
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Bgolearn is a flexible and extensible Python package for Bayesian Global Optimization (BGO). It is specifically designed to accelerate materials discovery via active learning and adaptive sampling strategies.
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).
- Bayesian Optimization Core: Supports single- and multi-objective optimization using GPR-based surrogate models.
- Materials Design-Oriented: Tailored for high-throughput experiments and structure–property optimization workflows.
- Active Learning Framework: Combines uncertainty sampling and exploration–exploitation balance strategies.
- Customizable Acquisition Functions: Includes EI, PI, UCB, and supports user-defined strategies.
- User Interface + Web Deployment: Works with BgoFace for intuitive web-based control.
- Code Walkthrough: Watch on BiliBili
- Sample Code + Datasets: CodeDemo Repository
| Name | Description |
|---|---|
| 🔗 Bgolearn | Core source code of the Bayesian Global Optimization framework |
| 🔗 MultiBgolearn | Extension for multi-objective optimization |
| 🔗 BgoFace | Graphical user interface (GUI) for interactive BGO |
| 🔗 CodeDemo | Example scripts and synthetic datasets |
| 🔗 Document | Official documentation site |
| 🔗 MLMD | A programming-free platform for ML-based materials design |
| 🔗 VSGenerator | Dynamic Virtual Space Generation Neural Network |
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}
}
Explore more works using Bgolearn on Google Scholar
We welcome contributions and suggestions! Please ⭐️ the repo Bgolearn if you find it helpful.
