{"repo":"Bin-Cao/Bgolearn","free":true,"listed":false,"github":"https://github.com/Bin-Cao/Bgolearn","clone":"git clone https://github.com/Bin-Cao/Bgolearn.git","description":"[NPJ Com. Mat.] Offical implement of Bgolearn","language":"Jupyter Notebook","stars":153,"topics":["expected-improvement","knowledge-gradient","material-design","bayesian-global-optimization","predictive-entropy-search","probability-of-improvement","upper-confidence-bound","entropy-based-approach","least-confidence","margin-sampling"],"license":"MIT","category":"ui-components","readme_excerpt":"Bgolearn A unified Bayesian optimization framework for accelerating materials discovery. Language: English 简体中文 日本語 한국어 Deutsch Playground: Interactive Bayesian optimization game --- Featured Introduction Bayesian Global Optimization is Chapter 1 of the Springer book An Introduction to Materials Informatics by Prof. Tong-Yi Zhang, Academician of the Chinese Academy of Sciences. The active-learning examples and results in this chapter are implemented with and depend on Bgolearn . Overview Bgolearn is a research-oriented Python framework for Bayesian Global Optimization (BGO) . It is designed for data-driven materials discovery, experimental design, and virtual screening, where each new measurement can be costly and every recommendation should be traceable. The framework brings surrogate modeling, uncertainty-aware acquisition, active learning, and candidate ranking into a single workflow. It supports both regression and classification tasks, so researchers can move from small experimental datasets to the next most informative material candidates with less custom glue code. Highlights - Unified workflows for regression, classification, active learning, and virtual screening. - Multiple surrogate models, including Gaussian process, SVM, random forest, AdaBoost, and MLP-based options. - Acquisition functions for noisy and noise-free optimization, including EI, AEI, EQI, REI, UCB, PoI, PES, and KG. - Candidate recommendation for minimization, maximization, and multi-objective rese","default_branch":null,"files":null,"tree":[],"storefront":"/r/Bin-Cao","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Bin-Cao/Bgolearn/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}