Performing experiments at well-defined conditions and first-principles based calculations constitute the basis of engineering research. In chemical engineering, these activities are aimed at e.g. the development and optimization of catalysts, reaction conditions and reactor configurations. In the chemical industry, 51 billion USD was spent in 2017 on research and development . This illustrates the importance of high quality data, however, obtaining accurate data is tedious and error prone. Design of experiments (DoE) can help by extracting maximal information with a minimum of effort , making sure that the time and resources are spent efficiently. By integrating machine learning with DoE, a more flexible and efficient DoE is achieved. This so-called "active machine learning" allows, in particular for highdimensional and highly-nonlinear phenomena, a more effective selection of experimental conditions .