Research in materials science requires large, multi-dimensional parameter spaces. The number of new chemical compounds with potentially attractive properties or chemical compounds that are candidates to fulfill targeted material properties are too numerous to be counted. This requires a paradigm shift in the way that we operate laboratories. We are heading towards automated and autonomeous laboratories that can investigate a large number of compositions in a short time or solve complex material problems guided by machine learning and artifical intelligence. We are providing the hardware and software solutions for the labs of the future.