An Autonomous Liquid-Handling Platform for ML-Driven Industrial Formulation Discovery

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 impossible due to the sheer number of possible compositions. To enable rational design of these materials, we must leverage theory, simulation, and machine learning (ML) tools to greatly reduce the expense of creating phase diagrams. Applying ML tools to scattering experiments requires a platform capable of autonomously creating and measuring samples with varying composition and chemistry. While there are numerous examples of robots which perform specific user facility operations, these systems tend to be bespoke and non-adaptable to new tasks. 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. Here we will highlight the design of the platform and our efforts in autonomous phase mapping of model formulations.