Complex liquid mixtures are the foundation of industries ranging from personal care products to biotherapeutics to specialty chemicals. While neutron and X-ray scattering methods are workhorse techniques for characterizing model formulations, the large number of components in many real products makes mapping the high-dimensional parameter space challenging due to the sheer number of possible compositions. To enable rational design of these materials, we have developed a highly adaptable sample environment that can be programmed to autonomously prepare and characterize liquid-formulations using neutron and X-ray scattering. Using this automated platform, we hope to explore the complex nature of molecular interactions that determine rheology, morphology, and efficacy in performance for surfactants, polymers, nanoparticles, and biotherapeutics. We seek to extend this platform with new measurement modalities and to develop active learning methods that leverage theory, simulation, and machine learning (ML) tools to greatly reduce the expense of creating phase diagrams and mapping formulation stability.

[1] Chem. Mater. 2023, 35, 3, 846-852

key words

Machine Learning; Small-Angle Scattering; Theory; Simulation; Surfactant; Nanoparticle; Polymer;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants