Lastly, we take a look at the distribution of the 29 experiments done using Bayesian optimization in Figure . From this distribution we see that we have managed to successfully predict and synthesize 3 novel formulations (accounting for the removal of the initial high performing seed), that have exceptional shear bonding strength to dentin. This result is highly significant because we have shown the ability to find novel formulations that can allow dental materials researchers to find multiple exceptional materials at a faster rate. Another important factor is that this realization can bring dental materials to market at a cheaper cost due to the reduction in experiments. From this we hope to help propagate the use of machine learning and techniques such as Bayesian optimization coupled with active learning to drive dental materials research.