How does the model evolve during this process? Having demonstrated the effectiveness of uncertainty-driven active learning to find compositions that form vesicles under low total amphiphile concentration, a natural question is how the model evolves during this process. In practice, monitoring this type of information is useful for guiding the decision about when to stop the exploration stage (AL 0 -AL 5) and begin the exploitation stage (AL 6) in the process. Figure shows the GPC model accuracy for AL 1 to AL 6, as AL 0 was not directly comparable using the GPR model. The data pool was randomly split to 80%