The AL 1 results (orange) show that 54% of samples had vesicles, and the total concentration was decreased since the objective function in BO is designed to lower the total concentration of amphiphiles (see Equation ). However, backtesting on the collected AL 0 and AL 1 data at this stage revealed that the GPR model tended to overfit and had poor generalizability to unseen data, because of a discontinuity in the penalty function for samples without vesicles (see discussion in Supporting Information). Similar backtesting with a GPC model did not have these problems, so the GPC model was used for all the subsequent AL rounds. This transforms the question from an optimization into a binary classification problem. Samples with more than zero vesicles were treated as positive samples, and others were treated as negative samples. The most uncertain samples (i.e., samples near the classification boundary) were selected for the subsequent round, starting with AL 2. First, 10 million samples were randomly selected within the input space bounds. Based on predicted probabilities from the GPC model, the most uncertain 48 samples were chosen as the next experimental plan. The uncertainty was evaluated by the entropy of each data point, which is defined in Equation S12 and Equation S13