While all synthetic problems presented in section 3.2 are subject to unknown constraints, the two constraints of the AgNP synthesis problem in section 3.1 are known, as they only depend on the flowrate input parameters. However, we chose to handle these two known constraints as if they were unknown, as the PF was not expected to be strongly restricted by the constraints. Solutions from the entire decision space, including infeasible points, were considered during the optimization process, relying on GP constraint learning to avoid suggesting infeasible candidates. Infeasible candidates suggested by the optimizer were then repaired afterwards (post-repair). This method is hence sensitive to the definition of the repair operator equations, which projects the infeasible solutions into the feasible space. The equations defining the constraints for the AgNP synthesis (see SI 1.1) project most of the infeasible points along the Qseed direction onto the constraint boundary (see SI 2.3). Since part of the constraint boundary corresponds to the POS, both algorithms benefit from the post-repair process to suggest more points close to the PF. However, this comes with a bias, as part of the unconstrained POS seems to be located at high QAA. As most of the high QAA infeasible points project towards a small region of the decision space, this leads to the oversampling detected at high 𝑦 2 (see