The complexity of MOOPs notably arises when dealing with conflicting objectives, as one needs to find not only one global optimum but an ensemble of solutions that simultaneously optimizes the different objectives. The optimal trade-off solutions, also called the Pareto Front (PF), are often used to help decision making on how to operate the HTE platform to produce the best material. Alternatively, knowledge extraction can be performed a posteriori to understand the relationship between each objective at the PF . However, the accuracy of knowledge extraction strongly depends on the sampling resolution at the PF during the optimization process. Therefore, special attention should be given to the PF exploration during the optimization process.