Bayesian Optimization (BO) presents an advanced alternative to traditional trial-and-error and design of experiments approaches for navigating design spaces. Notably, BO has been validated as a statistically robust method for optimizing material properties across a variety of materials . Central to BO are surrogate models, which are pivotal for modeling black-box functions that are expensive to evaluate and whose underlying functional forms remain unknown . This capability makes BO exceptionally suitable for challenges in dental materials research, where precise mathematical models that map formulation space to property space are not well-defined. BO demonstrates its strength in situations where the material property or goal is explicitly specified; for instance, maximizing the yield strength of a dental composite or minimizing its shrinkage. Driving the success of BO are two critical components: the surrogate model, which estimates the black-box function, and the acquisition function, which strategically guides the selection of subsequent experiments in the design space for iterative exploration. This iterative approach, known as active learning, ensures the prioritization of the most informative experiments at each iteration, thereby enhancing the efficiency of the exploration process.