Autonomous High‐Throughput Characterization of Liquid‐Liquid Phase Behavior

ABSTRACT

Self‐driving labs and data‐driven formulation have outpaced the characterization of liquid‐liquid miscibility and phase behavior, despite the role of such characterization in determining the stability and efficacy of complex formulations across diverse applications. Traditional characterization methods rely on labor‐intensive visual inspection or single‐proxy measurements that lack chemical generality, limit throughput, and provide only partial insight into phase behavior. Here, we report an automated platform that enables continuous, high‐throughput screening of liquid‐liquid phase behavior across diverse fluid chemistries. The device integrates an asymmetric capacitance sensor that is sensitive to the emergence and motion of phase boundaries, along with multi‐angle turbidimetry that quantifies cloudiness and emulsion stability, in a single flow‐through chamber. We demonstrate the classification of chemically diverse binary mixtures, resolution of real‐time phase separation kinetics, and identification of partial miscibility across compositions and temperatures. For multicomponent systems, we employ Gaussian‐process‐based active learning to autonomously map ternary phase diagrams in ∼2 h, together with a nonlinear programming framework that extracts tie lines in ∼5 min per line. By unifying miscibility classification, kinetic characterization, and thermodynamic mapping in a single automated workflow, the platform enables comprehensive phase behavior screening at the scale and throughput required for autonomous formulation discovery.