(361c) Autonomous Construction of Nanomaterial Phase Maps Using High-Throughput X-Ray Scattering

Automation in many of the experimental pipelines both at laboratory and central facilities has shifted the bottleneck to autonomously-driven data analysis and decision-making. Exponential growth in tools available for data-driven modeling resulted in the advent of self-driven laboratories (SDL) that aims to automate and accelerate the entire workflow starting from synthesis to characterization and device integration for emerging technologies and energy needs. Platforms based on solution-processible materials (polymers, colloids, and nanoparticles) are amenable to automation both at the synthesis and characterization levels. Techniques such as scattering and spectroscopy provide faster high-throughput alternatives to capture a signal of the underlying structure allowing us to construct composition-structure phase maps. Although obtaining a structure phase map from high throughput structural characterization data is a decade-old problem, many of the state-of-the-art approaches are not generalizable beyond their initial application in X-ray diffraction. In particular, small-angle scattering (SAS) used to probe the structure of nanoscale material results in a continuous signal where the key information is not just in peak positions but also the overall ‘shape’ of the curve. Thus, self-driven laboratories for soft materials should be equipped with tools that can process the shape of SAS curves into useful information for decision-making and optimization. In this talk, we present a novel approach that uses Riemannian metric geometry to develop a shape-based measure for analyzing SAS curves. We use this novel analysis tool in an SDL to build a phase map of the block copolymer system in an iterative closed-loop fashion. Our approach allows the decision-making to be built on top of multiple Riemannian metric fields each corresponding to an order parameter of the phase map. Using a case study of a novel polymer blend, we show that the learned order parameters are continuous and thus have an interpretation in terms of multi-phase transitions.