Accelerated (Re)Design of Sustainable Formulations with The Autonomous Formulation Lab

Industrial liquid formulations from electronics to nanoparticle coatings to drug delivery vehicles are often<br/>strikingly complex, with large numbers of components (10-100), complex multistep processing,<br/>and a wide variety of design requirements for a functional, sustainable, regulatory compliant product. This complexity often<br/>precludes physics-informed mapping between component fractions, processing, structure,<br/>leaving most formulation design to empirical trial-and-error or design of experiments strategies.<br/>I will discuss recent efforts by the Autonomous Formulation Laboratory (AFL) team at NIST to<br/>use machine learning driven, highly automated characterization to rapidly and intelligently map<br/>formulation phase space using structural characterization tools such as small-angle x-ray and<br/>neutron scattering (SAXS/SANS) together with secondary measurements, e.g. optical imaging,<br/>UV-vis-NIR and capillary viscometry. Our initial studies have resulted in an order of magnitude<br/>reduction in the time needed to map a model phase diagram and rapid cross-learning between model petroleum-based and biobased formulations. Other systems of interest from our personal care, biopharmaceutical, and alternative energy industrial collaborators will also be highlighted, including recent results on multimodal cooperative active learning.