To take full advantage of the rapid and accurate calculation of the reaction pathways for novel catalyst/substrate combinations and computational high-throughput screening, the automated localization of the transition structures is needed. Here, we demonstrate that the AIMNet2-Pd model allows double-ended minimum energy pathway searches, transition state optimizations, intrinsic reaction coordinate (IRC) calculations, and frequency analysis using learned energies, atomic forces, and Hessians. Although we demonstrate these capabilities for a single step of the Suzuki-Miyaura catalytic cycle, it is representative of AIMNet2-Pd's broader general performance. Our aim is to highlight the functionality that supports efficient reaction characterization via mechanistic modeling, which encompasses a set of challenging tasks for MLIPs due to the requirements for predicting atomic interactions of significantly off-equilibrium geometries.