The experimental workflow established in this work consumes minimal amounts of materials and is suitable for broad parameter variation while maintaining good comparability of individual experiments on it, to maximize the data output in a limited set of experiments for potential use in machine learning. We choose a combinatorial approach to material synthesis to allow for a high variety of structural motives and investigate the behavior of the final material as a function of structural composition. With the chemical compositions, the solvent, the ratio between monomers and crosslinkers and the pH value, we have a huge set of independently variable parameters to achieve a maximum of different gel property combinations between the swelling/size and degradation speed. For the miniaturized preparation of the hydrogel pads a nanoparticle coated glass substrate modified with hydrophilic spots confined by superhydrophobic borders was used. These droplet microarrays were fabricated by photolithography, using UV induced thiol-ene click reaction between surface vinyl-groups and either mercaptoethanol (hydrophilic) or 1H,1H,2H,2Hperfluorodecanethiol (PFDT) (hydrophobic). The final array had a format of 14 columns and 48 rows of hydrophilic 1 mm squares on a 2.5 cm x 7.5 cm microscopy glass slide. Here we achieve an experimental density of more than 35 experiments, requiring just 130 nL solutions each, per square centimeter, more than tenfold the density of standard 384 well plates.