[2604.01328] Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial

Computer Science > Machine Learning arXiv:2604.01328 (cs) [Submitted on 1 Apr 2026] Title:Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial Authors:Zhongwei Yu, Rasul Tutunov, Alexandre Max Maraval, Zikai Xie, Zhenzhi Tan, Jiankang Wang, Zijing Li, Liangliang Xu, Qi Yang, Jun Jiang, Sanzhong Luo, Zhenxiao Guo, Haitham Bou-Ammar, Jun Wang View a PDF of the paper titled Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial, by Zhongwei Yu and 13 other authors View PDF Abstract:Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known knowledge and exploration of uncharted domains to eliminate guesswork and manual trial-and-error. We first frame scientific discovery as an optimisation problem, then unpack BO's core components, end-to-end workflows, and real-world efficacy via case studies in catalysis, materials science, organic synthesis, and molecule discovery. We also...