Quantifying Experimental Characterization Choices in Optimal Learning and Materials Design

We consider the choices and subsequent costs associated with ensemble averaging and extrapolating experimental measurements in the context of optimizing material properties using Optimal Learning (OL). We demonstrate how these two general techniques lead to a trade-off between measurement error and experimental costs, and incorporate this trade-off in the OL framework. As a first contextual example, we study the effect of ensemble size in determining the most accessible regions of an RNA molecule. A second example considers the impact of the number and frequency of initial measurements used to extrapolate a measure of nanoemulsion stability. In both cases, we use OL simulations to determine the optimal choice of these characterization parameters by minimizing an associated total experimental cost.