Bayesian Optimization (BO) presents a promising solution for both tasks at hand. Firstly, its data-driven approach accelerates the process of discovering novel formulations. Secondly, the inherent interpretability of the mathematical model utilized in BO enables scientists to grasp the contributions from each constituent with clarity. A third benefit that is particularly relevant to a formulation space relying on proprietary monomers is A significant benefit of this approach is its applicability to the experimentation with new resins. Given that resins are often proprietary with undisclosed full structures, formulation scientists are constrained to trial-and-error methods due to limited chemical or domain-specific knowledge. BO circumvents this limitation by leveraging correlations between unknown components and experimental results, thereby streamlining the investigation of new resins as they become available.