In an ideal case, active machine learning is coupled with a flexible automated experimental unit or are even equipped by a robot . In this way control and optimization of the performance of the experiments can become optimal, and thus saving valuable time and effort. Automated experimental units are being increasingly applied for molecular synthesis and chemical engineering but these units are not yet commonplace . One requirement of automated robotic units is that they should be reconfigurable . They moreover should have a broad application range and not be limited to the investigation of a single reaction type or narrow temperature range. The use of automated units is of course not self-evident as these often are expensive and currently not well-suited for every problem. For example, despite past efforts the automated synthesis and testing of catalysts is a challenging task, definitely when studying a broad design space . By coupling these systems with active machine learning techniques, a huge time saving is expected for experimental campaigns, which will speed up reaction and catalyst optimization, and the acquisition of scientific knowledge. A last threshold of these automated units is of course the question of safety of these units. By expanding the catalyst or reaction design space, safety concerns rise as this increases the probability of undesired reactions to occur. Therefore, a good chemical knowledge is still required when employing these units to identify and incorporate the safety constraints. The definition of safety constraints again requires a close collaboration between experimental experts and machine learning scientists.