The Autonomous Formulation Laboratory: Macromolecular Formulation Discovery with Multimodal Measurements

Liquid formulations are ubiquitous in products ranging from deicing liquids and fuels/lubricants to biologic drugs, shampoo, and food/beverage ingredients. All these products require precise tuning of 10s-100s of components to produce a desired product: viscosity modifiers, surfactants, dyes, fragrances, flammability inhibitors, etc. While scattering methods (SAXS, SANS, WAXS) are workhorse techniques for characterizing model formulations, they have not been widely used to characterize real products, largely because the large number of components often precludes rational mapping between component fractions, structure, and product stability. Multimodal characterization and machine learning (ML) tools promise to greatly reduce the expense of exploring the stability boundaries of a particular, desirable phase in highly multicomponent products. This talk will describe the development of the Autonomous Formulation Laboratory, a highly adaptable platform capable of autonomously synthesizing and characterizing liquid mixtures with varying composition and chemistry using x-ray and neutron scattering in concert with other measurements. Highlights will include our development of model-free analysis approaches for x-ray scattering data, studies of surfactant micelle extension, and closed-loop AI-guided exploration of composite nanoparticle synthesis for coatings development. I will further discuss our ongoing efforts to incorporate a multimodal suite of secondary measurements such as optical imaging, UV-vis-NIR and capillary rheometry to provide greater-than-sum-of-parts materials characterization.