In order to increase the efficiency of workflow and to demonstrate the full potential of the automated platform for materials design and target-property optimization, we integrated methods of machine learning into the high-throughput screening process, sequentially suggesting new compositions and parameters for hydrogel synthesis. Apart from optimizing (maximize or minimize) droplet size, intensity and UV degradation, we furthermore performed a more general optimization aiming to increase the range of achievable properties, i.e. initial droplet size and degradation lifetime. We prepared a dataset that correlates composition, i.e.