Interpreting Neutron Reflectivity from Thin Films of Block Copolymers using Neural Networks

Thin films of ionic polymers are of great technological interests due to their relevance to solid polymer electrolytes and energy storage. There is active research on controlling morphology in these films to modify ion transport, a process affected by adsorption of ionic groups on conductive surfaces. Visualization of these adsorbed layers (~5-10 nm) is not a trivial task and requires characterization tools capable of capturing vertical and lateral structure in thin films. Grazing incidence neutron scattering is one such tool, which can provide information about vertical (specular reflection) and lateral structures (Off-specular scattering (OSS)) of adsorbed layers of ionic polymers.

While extraction of information about structure from specular neutron reflectivity has become a routine task, measurement and analysis of off-specular scattering has remained complicated. Here we have developed a machine learning (ML) workflow that uses both simulated and experimental data to learn a relation between the structure of sample thin films containing ionic polymers and the corresponding off-specular spectra. In this talk, we will present an application of the workflow in improving a model for understanding the electromechanical responses of ionic polymers, greatly expediting the process of finding desired structures for users.