The discovery and development of new materials is vital for both sustaining and technologicallyadvancing our society. Computational meth-ods, including electronic structure calculations, molecular simulations, and materials informatics/machine learning, can predict the properties of materials and thus be employed to optimize, screen, and design new materials rapidly and costeffectively-accelerating the rate of materials optimization and discovery. Bayesian optimization (BO) combines supervised machine learning, uncertainty quantification, and decision-making algorithms to automatically and efficiently design a sequence of experiments, in the lab or a computer simulation, to find materials with an optimal property for some application. Given (i) a pool or space of candidate materials and (ii) an experimental protocol (in the lab or a simulation) to measure/evaluate/predict the relevant property of a material, BO iteratively designs experiments (i.e., chooses materials to subject to an experiment) to find the optimal material with the fewest (costly) experiments. The two ingredients of BO for automated experiment planning are: