The applications of active machine learning span all length scales of chemical engineering from ab initio calculations , material, molecule and catalyst design , reaction design up to reactor design . For example, the design of catalysts is an important asset in achieving carbon neutrality as catalysts can enable more sustainable processes, and increase the energy efficiency of chemical processes in general. However, nowadays their design is still deemed an art, mainly relying on high-throughput screening and limited theoretical relations such as the Sabatier principle and linear scaling relations . This makes catalyst design prone to human bias as researchers tend to exploit catalyst designs which are known to work but this hampers real breakthroughs . With active machine learning, this human bias is removed and a substantially larger fraction of the catalyst space can be studied. Currently, the applications of active machine learning in catalysis only consider a limited design space varying only the catalyst composition while maintaining the catalyst structure. E.g., Zhong et al. performed Bayesian optimization on DFT calculations to identify and synthesize promising electrocatalysts for the reduction of CO2 , whereas Nugraha et al.