Density functional theory (DFT) has emerged as a crucial tool in computational chemistry over the last decades, revolutionizing the field of heterogeneous catalysis by providing an effective approach to predict energetic properties . This quantummechanical (QM) modeling method offers insight into surface chemistry and catalysis . First-principles-based multiscale modeling provides the bridge between experiments and theory that enables optimizations of catalysts and reactors . The DFT developments combined with advances in computational power have led to a surge of large-scale open-source databases, such as the Materials Project , the Open Catalyst Project (OCP) , the Novel Materials Discovery Laboratory (NoMaD) , Catalysis Hub , Automatic FLOW for Materials Discovery (AFLOW) , CatApp , and the Open Quantum Material Database (OQMD) . These databases have enabled the proliferation of energetic data, which has allowed a deeper understanding of catalytic processes at the atomic level.