Bgolearn 3.0.0

A Bayesian global optimization package for material design

Bgolearn : a powerful Bayesian Global Optimization package specifically designed for materials discovery. This document is written and produced by [Bin Cao](https://bin-cao.github.io/) to help new learners master the basics of Bayesian Optimization and use Bgolearn to solve real-world optimization problems.

Bgolearn is a Python package developed by [Bin Cao](https://bin-cao.github.io/) at Hong Kong University of Science and Technology (Guangzhou) that implements state-of-the-art Bayesian optimization algorithms for both single-objective and multi-objective optimization. It’s particularly powerful for materials discovery, where experiments are costly and time-consuming.

Key Features: - Single-objective optimization with multiple acquisition functions - Multi-objective optimization via MultiBgolearn - Materials-focused design and applications - Flexible surrogate model selection - Bootstrap uncertainty quantification

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Quick Usage Example from Bgolearn.BGOsampling import Bgolearn import pandas as pd

# Load your data data = pd.read_csv(‘data.csv’) X = data.iloc[:, :-1] y = data.iloc[:, -1]

# (Optional) Provide virtual samples for screening vs = pd.read_csv(‘virtual_data.csv’)

# Create and configure optimizer optimizer = Bgolearn() model = optimizer.fit(data_matrix=X, Measured_response=y, virtual_samples=vs)

# Run Expected Improvement acquisition candidates = model.EI()

### Support & Contribution Author & Maintainer: Dr. Bin Cao (CaoBin) — email: [e-mail].

### Collaboration Welcome: Open for issues, pull requests, and research partnerships.