An overview of the canonical Suzuki-Miyaura catalytic cycle, the focus of this study, is shown in Figure . This molecular Pd-catalyzed pathway proceeds through three main steps i) oxidative addition, ii) transmetalation, and iii) reductive elimination to form a C-C bond between two molecular species (denoted R1, R2). Characterizing the energetics of the cross-coupling intermediates and transition structures is a routine task in computational catalyst design and one of the applications of AIMNet2-Pd. Intermediate species of interest in this process are PdR1 (4) and PdR1R2 (7) compounds, the PdR1|BR2 complex (5), and the resultant R1R2 product (9P). To demonstrate the predictive power of our MLIP, we curated a test set of 45 Pd-catalysts outside of the training set with diverse phosphine ligands and computed their formation energy (Figure ). The ligand species include monodentate phosphorous (III) donors, Buchwald-type ligands, and P,P-bidentates, which were selected and modeled based on the list of widely used ligands assembled by Durand and Fey. All species included in the analysis of Figure are summarized in Table of the Supplementary Information. The mean absolute error (MAE) of AIMNet2-Pd's predicted relative interaction energies, i.e., the difference in energetics between the intermediate and the isolated reactants, compared to reference DFT calculations (see Methods), spans 1.7-2.2 kcal mol -1 (~2.5% error). For comparison, general purpose MLIPs, such as those used for non-reactive conformers of organic molecules, typically have an accuracy on the order of 1.0 -2.0 kcal mol -1 , where the slightly larger deviation observed here is expected for DFT calculations of organometallics. The AIMNet2-Pd interaction energy and force calculations require less than a second, enabling the elucidation of structure and energetics at every step in the C-C coupling cycle for a large number of potential catalyst/substrate combinations. The consistently high accuracy across the individual mechanistic steps suggest that the model can be highly effective for high-throughput Pd-catalyst discovery with minimal to no fine-tuning. As will be shown later, the model has broad transferability and high prediction accuracy for varied mechanistic steps and diverse catalytic species. The speed of AIMNet2-Pd predictions is insensitive to the type of reference electronic structure calculations that the AIMNet2 model is trained to. Therefore, follow-up transfer learning with expensive and chemically accurate calculations such as coupled cluster methods, stand to offer speed ups of ~10 6 -10 7 , i.e., reducing simulation times from weeks or longer to only seconds on a consumer-grade computer.