In this study we report a high-throughput methodology based on miniaturized experiments and machine learning for accelerated materials property optimization. We apply it to synthesize a library of approximately 1000 hydrogels with different binary compositions, based on the combination of acrylic and methacrylic monomers and crosslinkers. We investigate their respective properties, in particular photostability. Hydrogels as an exemplary material class for screening were chosen due to their large chemical space as well as their promising nature as biocompatible biomaterials of the future and thus a broad field of potential applications. Due to the high water content integrated into a polymeric network, they strongly resemble tissue and extracellular matrix´s properties, making them promising candidate materials for biomedical engineering, soft robotics, tissue engineering and microfluidics. Multi-materials systems of photodegradable hydrogels are of particular interest for encapsulation and controlled release of cargo, such as drugs or cells, or for the creation of degradable scaffolds for 3D cell culture. In previous reports we investigated inherently photodegradable hydro-and organogels based on methacrylate monomers and crosslinkers without the need of photosensitizing groups , which have been proven applicable e.g. in 3D cell culture. Since their degradation speed was shown to be highly dependent on the polarity of the monomers and the crosslinking density, an evaluation of a variety of combinations can cover a large area of desired material properties.