Catalysis is at the heart of efficient chemical processes and is a pathway to transition from fossil energy and chemical resources to renewables, granting a sustainable development. However, catalysts are developed mostly via empirical approaches and finding the underlying rationalization behind a success of a particular catalyst formulation remains a formidable challenge. The combined emergence of machine learning, high-throughput experimentation (HTE) and robust computational tools offers possibilities that could revolutionize catalyst design and process implementation. The aim of this project is to combine the efficiency of the HTE methods with the machining learning towards computer-guided prediction of catalysis research and rational design. As a prototypical example, we propose to explore Schrock-type metathesis homogeneous and heterogeneous catalysts to generate a training set for machine learning via high-throughput experimentation. The resulting catalytic data will be evaluated by computational chemistry to identify key parameters determining catalytic activity. With the help of multivariate descriptors, machine learning and this computational approach, an artificial neural network (ANN) will be targeted to unravel the trends in influencing catalysts performance, suggesting potential active metathesis catalysts and providing concepts in designing new generation of catalysts. With this tool and methodology in hand, the plan would be to expand this study on other reactions such as olefin oligomerization and epoxidation. The ultimate goal of this PhD is to establish a methodology to discover and develop more robust catalysts and to open the door to the “digitalization” of catalysis research.