Machine learning enhanced computational reverse-engineering analysis for scattering experiments (CREASE) of soft materials to establish structure-property relationships

Structural characterization is a critical step to establishing material design-structure-property relationships. To understand how the molecules assemble into structures and the morphologies they adopt at equilibrium and upon processing, one needs structural characterization at multiple length scales. Small angle scattering (SAS) enables 3D structural characterization across a wide range of length scales. The analysis of SAS results typically relies on fits using analytical models that are applicable for conventional shapes (e.g., spheres, cylinders, vesicles, etc.). With access to new soft materials chemistries and processing techniques, however, researchers observe unconventional structures for which existing analytical models are either too approximate or not applicable. Thus, there is a need for SAS analysis methods that do not rely on fitting the scattering profiles with analytical models. We have addressed this need with a new computational approach – Computational Reverse-Engineering Analysis for Scattering Experiments, CREASE. I will present how we use machine-learning enhanced CREASE to analyze SAS results from binary mixtures of spherical nanoparticles with varying composition, nanoparticle size distribution, and extent of mixing/aggregation. I will also show how we validate and then use the outputs from CREASE, in particular the 3D real-space configurations, as input to other computational methods that then predict macroscopic properties (e.g., optical response).