The NIST Autonomous Formulation Laboratory—Accelerating Liquid Formulation and Reaction Landscape Exploration with AI and X-Ray/Neutron Scattering

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. This 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. Highlights will include closed-loop AI-guided exploration of composite nanoparticle synthesis for coatings development and bioformulation exploration incorporating neural networks into scattering pattern classification. 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.