Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial

2026-04-01

AI Summary

Traditional scientific discovery relies heavily on inefficient trial-and-error experimentation, often overlooking critical insights. This work reframes the scientific research process as an optimization problem and proposes a Bayesian optimization framework that employs Gaussian process surrogate models and acquisition functions to autonomously guide experimental design, achieving an efficient balance between exploration and exploitation. The core contributions lie in extending four key techniques specifically for scientific discovery: batch experimental design, heteroscedastic modeling, context-aware optimization, and human-in-the-loop collaboration. Empirical evaluations across catalysis, materials science, organic synthesis, and molecular discovery demonstrate that the proposed approach substantially enhances experimental efficiency and accelerates the pace of scientific discovery.

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 cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration. Tailored for a broad audience, this tutorial bridges AI advances in BO with practical natural science applications, offering tiered content to empower cross-disciplinary researchers to design more efficient experiments and accelerate principled scientific discovery.

Problem

• scientific discovery
• experimental efficiency
• hypothesis testing
• resource waste
• trial-and-error

Innovation

• Bayesian Optimization
• surrogate modeling
• acquisition function
• batch experimentation
• human-in-the-loop