Information-driven experimental design in materials science

Abstract

Optimal experimental design (OED) aims to maximize the value of experiments and the data they produce. OED ensures efficient allocation of limited resources, especially when numerous repeated experiments cannot be performed. This chapter presents a fully Bayesian and decision theoretic approach to OED—accounting for uncertainties in models, model parameters, and experimental outcomes, and allowing optimality to be defined according to a range of possible experimental goals. We demonstrate this approach on two illustrative problems in materials research. The first example is a parameter inference problem. Its goal is to determine a substrate property from the behavior of a film deposited thereon. We design experiments to yield maximal information about the substrate property using only two measurements. The second example is a model selection problem.We design an experiment that optimally distinguishes between two models for helium trapping at interfaces. In both instances, we provide model-based justifications for why the selected experiments are optimal. Moreover, both examples illustrate the utility of reduced-order or surrogate models in optimal experimental design.