A comparison of the AIMNet2-Pd and DFT vibrational frequencies calculated at the transition state geometry is displayed in Figure . Most vibrational peaks agree within a few wavenumbers, demonstrating that the two approaches produce similar Hessians. Importantly, this indicates that AIMNet2-Pd can predict vibrational energy contributions to thermodynamic free energy calculations, which are crucial for accurate predictions of reaction selectivity and rates. Taken altogether, it has been shown the AIMNet2-Pd can reliably drive reaction characterization tasks required to profile Pd-catalyzed transition pathways at a drastically reduced cost. The combination of automated transition structure search and fast structure optimization highlights the potential for high-throughput computational screening of catalyst/substrate combinations that will be described in the next Section.