Accelerating the Discovery of Abiotic Vesicles with AI-Guided Automated Experimentation

Abstract

The first protocells are speculated to have arisen from the self-assembly of simple abiotic carboxylic acids, alcohols, and other amphiphiles into vesicles. To study the complex process of vesicle formation, we combined laboratory automation with AI-guided experimentation to accelerate the discovery of specific compositions and underlying principles governing vesicle formation. Using a low-cost commercial liquid handling robot, we automated experimental procedures, enabling high-throughput testing of various reaction conditions for mixtures of seven (7) amphiphiles. Multitemplate multiscale template matching (MMTM) was used to automate confocal microscopy image analysis, enabling us to quantify vesicle formation without tedious manual counting. The results were used to create a Gaussian process surrogate model, and then active learning was used to iteratively direct the laboratory experiments to reduce model uncertainty. Mixtures containing primarily trimethyl decylammonium and decylsulfate in equal amounts formed vesicles at submillimolar critical vesicle concentrations, and more than 20% glycerol monodecanoate prevented vesicles from forming even at high total amphiphile concentrations.