  To facilitate the design and optimization of nanomaterials for a given
application it is necessary to understand the relationship between structure
and physical properties. For large nanomaterials, there is imprecise structural
information so the full structure is only resolved at the level of partial
representations. Here we show how to reconcile partial structural
representations using constraints from structural characterization measurements
and theory to maximally exploit the limited amount of data available from
experiment. We determine a range of parameter space where predictive theory can
be used to design and optimize the structure. Using an example of variation of
chemical composition profile across the interface of two nanomaterials, we
demonstrate how, given experimental and theoretical constraints, to find a
region of structure-parameter space within which computationally explored
partial representations of the full structure will have observable real-world
counterparts.
