Get an overview of the extent of a RO parameter space. See which parameter settings have already been explored and which have not. Get an overview of an AI model's predictions of the parameter space. See whether a model focuses on exploring unseen regions of a parameter space or whether it maximizes outcomes within known, high-performing areas. (C1) Exponentially growing parameter space: RO datasets grow exponentially with regard to the parameters that are optimized. For example, if we want to optimize parameters for temperature, base, and solvent, and for each of the parameters we define five values to optimize on, the whole combinatorial parameter space is 5 * 5 * 5 = 5 3 = 125; if we choose to also optimize for 10 different reagents, the parameter space already amounts to 1250 different experiments. This exponential growth soon becomes a challenge from a computational resource and human perception perspective.