Bayesian Optimization for Self-Driving Materials Laboratories: From Algorithms to Physics-Informed Workflows
A review of Gaussian-process-based BO and the formulation of materials goals as quantitative objectives, and particular emphasis is placed on physics-informed Bayesian optimization (PIBO), in which domain knowledge enters through representations, priors, kernels, acquisition functions, and constraints.