The AIMNet2-Pd model shows remarkable versatility, accurately predicting the behavior of catalyst structures beyond its training set, including both minima, transition states and reactive intermediates. We are particularly encouraged by its success in modeling challenging cases like dinuclear Pd-compounds and are actively working to extend it to other Pd-catalyzed transformations such as Heck reactions. These promising results suggest that MLIPs could evolve into a powerful general-purpose tool for rapidly predicting outcomes of organometalliccatalyzed reactions. Looking ahead, AIMNet2-Pd opens new possibilities for integrating computational catalyst screening with automated synthesis platforms. By lowering the computational barrier to mechanistic modeling, our work democratizes advanced catalyst design tools and supports the ongoing revolution brought by high-throughput chemistry. The foundation established here points toward a future where machine learning potentials become standard tools for accelerating catalyst discovery across synthetic chemistry.