Constraints on nanomaterial structure from experiment and theory: Reconciling partial representations

TL;DR

This paper presents a method to reconcile partial structural data of nanomaterials using experimental constraints and theory, enabling better design and optimization despite limited structural information.

Contribution

It introduces a framework to integrate experimental and theoretical constraints to identify plausible nanomaterial structures from partial representations.

Findings

Defined a parameter space for nanomaterial structures

Demonstrated the approach with chemical composition profile variation

Showed how to find structures with real-world counterparts

Abstract

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.