The goal of MLIP models is to provide potential energy surface representations that relate molecular geometry to the characteristic interactions derived from appropriate electronic structure calculations. In recent years, MLIPs have matured from being tools used primarily for neutral gas-phase small molecules to models with exceptional transferability, applicable to systems with significant long-range interactions, and/or include equivariant mathematics. Despite advances, MLIPs that can evaluate metal-containing molecular catalyzed reactions have yet to be reported. This hinders their application to many reaction classes of interest in synthetic organic chemistry. To address this limitation, AIMNet2-Pd is trained to ensure the predicted potential energy surface describes reaction pathways, thus allowing Pd-catalyzed transformations analyzed in silico. Importantly, performing reaction characterization tasks, such as transition structure optimization, with AIMNet2-Pd comes at the minimal cost of ML inference.