Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Bayesian optimisation (BO) is a sample‐efficient strategy for optimising expensive, noisy, black‐box functions, challenges that are common in experimental sciences where each experiment may involve significant cost, time, and effort. Despite its theoretical strengths, BO remains under‐utilised in experimental disciplines, in part due to its perceived mathematical complexity and lack of practical guidance tailored to non‐specialists. This tutorial provides a comprehensive yet accessible introduction to BO, with an emphasis on implementation in chemistry, materials science, and related fields. We cover the core components of BO, including surrogate modelling with Gaussian Processes, acquisition functions, and sequential decision‐making. In addition, we discuss practical extensions for handling noise, constraints, multi‐objective, and high‐dimensional optimisation, mixed‐variable spaces, batch processing, and computational scalability. Strategies for incorporating domain knowledge and deploying BO in automated experimental workflows are also presented. Throughout, we provide practical advice, highlight useful software, and emphasise conceptual understanding over mathematical formalism. As laboratory automation and data‐driven discovery become increasingly central to modern science, we argue that BO will play a transformative role in accelerating intelligent experimentation. The goal of this paper is to lower the barrier to entry and equip the reader with the knowledge needed to apply BO effectively in their own work.