The experiment-analysis-plan feedback loop 12 that constitutes BO (see Fig. ) iterates through (i) conduct an experiment to obtain a (material, property) observation, (ii) update the surrogate model in light of this new experimental data, then (iii) select the next material for an experiment by maximizing the acquisition function. Compared to random search, BO leverages the surrogate model to make principled decisions balancing the exploration-exploitation trade-off so as to uncover the optimal material early in the sequential search. Because the acquisition function and optimization algorithm negate the need for humans to design the experiments inside the experimentanalysis-plan feedback loop, BO can orchestrate autonomous, "self-driving" labs that employ automated instrumentation and/or robots to conduct a sequence of experiments with the goal of resource-efficient materials discovery and optimization.