We identify the most promising catalysts, as well as the top building blocks (both site-specific and siteagnostic) and structural patterns that contribute favorably to the reaction thermodynamics. To accelerate the high-throughput screening, we also train machine learning models that accurately model the Gibbs free energies of these reactions. Even with models trained on partially optimized geometries, we are able to achieve similar model performance, thus significantly reducing the time required for first-principles computational studies. We aim to utilize these valuable insights into these systems to pursue promising candidates via future experiments.