Such platforms aim to streamline and enhance the efficiency of scientific experimentation, research, and analytical processes. SDLs can accelerate the rate at which advanced materials, functional molecules, and industrial processes are designed by enhancing productivity, throughput, accuracy, and reproducibility. Early-stage SDLs have targeted diverse research and development goals, including chemical reaction and process optimization, the design of nanomaterials, and light-harvesting materials, to name a few. The cornerstone of an SDL is its decision-making algorithm (here informally referred to as its "brain"), which is typically implemented as a data-driven experiment planning strategy. Compared to less dynamic strategies such as Design of Experiment, data-driven approaches leverage feedback from previously completed experiments to inform subsequent recommendations of experimental parameters, resulting in superior sample efficiency. Although many such strategies have been proposed, including gradient-based optimizers, evolutionary strategies, and reinforcement learning, Bayesian optimization (BO) has recently emerged as the most popular choice. BO is a sequential optimization strategy for expensive-to-evaluate black-box functions based on machine-learned approximations of the target objective being optimized.