BgoFace

A user-friendly active learning platform built on Bgolearn for autonomous experimentation and accelerated materials optimization.

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BgoFace is a user-friendly interface developed for the Bgolearn framework, led by Cao Bin and supported by the related Bgolearn publications. Designed to accelerate materials discovery, BgoFace simplifies Bayesian global optimization (BGO) workflows by bridging the gap between experimental and computational domains.

With intuitive controls, integrated support for experimental constraints, and seamless access to active learning algorithms, BgoFace empowers users to conduct efficient materials exploration—without requiring deep expertise in machine learning.

Special thanks to Mr. Tianliang Li, Mr. Siyuan Liu, and to the guidance of Prof. Tong-Yi Zhang and Prof. Lingyan Feng.

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Resource Links

Resource	Description	Link
Bgolearn	Core Bayesian optimization framework	github.com/Bin-Cao/Bgolearn
MultiBgolearn	Bgolearn multi-object module	github.com/Bin-Cao/MultiBgolearn
BgoFace	Official Bgolearn GUI	github.com/Bgolearn/BgoFace
CodeDemo	Example code and datasets	github.com/Bgolearn/CodeDemo

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Design Materials Using BgoFace

BgoFace allows users to design, visualize, and analyze material systems via an intuitive graphical interface.

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Code Tutorial

Get started quickly by watching our step-by-step video tutorial: BiliBili: Intro to BgoFace

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Download the Application (Windows)

You can directly download the latest pre-built version of BgoFace for Windows from our Releases Page.

Steps:

1. Navigate to the Releases Section.
2. Download the .exe file from the latest release.
3. Run the file — no installation is required!

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Architecture of BgoFace

This diagram outlines how the components of BgoFace interact, from user input to backend computation:

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UI Encapsulation Guide

To create a standalone desktop version of BgoFace yourself:

1. Install Required Packages

   pip install pyqt5 pyinstaller

2. Build Executable with PyInstaller

   pyinstaller -F -w --add-data "Images;Images" main.py

   • -F: Bundle into one file
   • -w: Suppress console window
   • --add-data: Include additional assets like images

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Acknowledgement

If you use the code or data from this repository, please cite our related research publication.

@article{Cao2026Bgolearn,
  author    = {Bin Cao and Jie Xiong and Jiaxuan Ma and Yuan Tian and Yirui Hu and Mengwei He and Longhan Zhang and Jiayu Wang and Jian Hui and Li Liu and Dezhen Xue and Turab Lookman and Jun Wang and Tong-Yi Zhang},
  title     = {Bgolearn: a unified Bayesian optimization framework for accelerating materials discovery},
  journal   = {npj Computational Materials},
  year      = {2026},
  volume    = {12},
  pages     = {Article xxx},
  doi       = {10.1038/s41524-026-02226-3},
  url       = {https://doi.org/10.1038/s41524-026-02226-3}
}

@article{li2025optimize,
  title     = {Optimize the quantum yield of G-quartet-based circularly polarized luminescence materials via active learning strategy-BgoFace},
  author    = {Li, Tianliang and Chen, Lifei and Cao, Bin and Liu, Siyuan and Lin, Lixing and Li, Zeyu and Chen, Yingying and Li, Zhenzhen and Zhang, Tong-yi and Feng, Lingyan},
  journal   = {Materials Genome Engineering Advances},
  volume    = {3},
  number    = {3},
  pages     = {e70031},
  year      = {2025},
  publisher = {Wiley Online Library}
}

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License & Usage

© 2024 Bgolearn Development Team. All rights reserved.

This software is for academic and research use only. Commercial use is strictly prohibited and subject to enforcement.