Overview of the Bgolearn Project
Bgolearn is an advanced open-source platform designed to optimize material design using cutting-edge Bayesian optimization techniques. The project comprises several key modules, each addressing different aspects of material optimization and analysis.
Key Components
1. Bgolearn
The core module of the project, Bgolearn, provides tools for regression, classification, and efficiency testing. It supports a range of optimization methods and regression models to enhance data analysis and material design processes.
Features:
- Regression: Utilizes various regression models for predicting material properties.
- Classification: Implements classification algorithms for material categorization.
- Efficiency Testing: Evaluates the efficiency of different optimization strategies.
2. MultiBgolearn
An extension of the Bgolearn module, MultiBgolearn focuses on multi-objective Bayesian optimization. It is designed to tackle problems requiring the simultaneous optimization of multiple objectives, leveraging advanced Bayesian techniques to address complex trade-offs.
Features:
- Multi-Objective Optimization: Facilitates the simultaneous optimization of multiple objectives.
- Bayesian Optimization: Flexible surrogate model selection, allowing the user to choose from a range of models such as RandomForest, GradientBoosting, SVR, GaussianProcess, and more.
3. BgoFace
BgoFace offers a user-friendly graphical interface for interacting with Bgolearn's functionalities. It simplifies running optimization tasks, visualizing results, and managing data, making it accessible even to users with minimal programming experience.
Features:
- Graphical User Interface: Provides an easy-to-use interface for accessing Bgolearn features.
- Integration: Seamless integration with Bgolearn for running and visualizing optimization tasks.
Installation
To install the Bgolearn suite, use the following commands:
pip install Bgolearn
pip install MultiBgolearn
Documentation
Contact
For more information or support, please reach out to us:
- Email: bcao686@connect.hkust-gz.edu.cn (Dr. Cao)
- GitHub: Bgolearn GitHub Repository
License
This project is licensed under the MIT License. See the LICENSE file for more details.
