Here, we apply the subgroup-discovery (SGD) artificialintelligence local approach to a hybrid data set obtained from HTE & theory to identify key physicochemical descriptive parameters and constraints on their values, i.e., rules, which are particularly associated with high performance. The reactivity measured by HTE is used as target in the SGD analysis. The temperature and composition-dependent physicochemical properties evaluated with density-functional-theory (DFT) calculations are used as candidate descriptive parameters.