Throughout the last decade, machine learning methods have proven useful in many research areas such as material science , chemistry , biology and drug discovery. One major advantage of machine learning methods is the utilization of the vast amount of data generated in decades of research to achieve faster progress in science, e.g. by improving the virtual design of materials , speeding up optimization processes or yielding a better understanding of hidden relations in data and thus fundamental processes. With continuing progress in ML research, the research speed of manual lab experiments and thus the generation of new data is not sufficient any longer to sustain the amounts of data needed to efficiently make use of new ML methods. Thus, increasing the throughput of experimental pipelines comes as a logical consequence and drives the research community to parallelize experiments in order to speed up data generation. Due to the increased need of reagents, consumables and effort to achieve this in a conventional and manual fashion, automated high-throughput platforms and miniaturization are becoming of central importance.