The AIMNet2-Pd model is built using the recently reported AIMNet2 (2nd generation atoms-inmolecules neural network potential) MLIP architecture. Details on the underlying mathematical transformations, design rationales, and software workflow can be found in the original manuscript by Anstine, Zubatyuk, and Isayev. Briefly, AIMNet2 transforms atomic coordinates, atomic numbers, and the total charge of a system into information-rich atomic descriptor vectors. This is carried out using a message passing scheme based on scalar and vector geometric convolutions with neighboring atoms as well as iterative atomic partial charge refinement. The AIMNet2 architecture is unique in its ability to make generalized predictions for both neutral and charged molecular species. Here the assigned partial charges are learned while being rigorously restrained to match the input net charge. Long-range interactions can also be explicitly included, as shown in the pre-trained AIMNet2 model. However, in this work we elected not to include explicit treatment of electrostatics. This design decision was informed by the fact that long-range interactions beyond the local cutoff are predominantly screened by the implicit solvent model used in generating training data (see next Section). Moreover, our initial testing of models with and without explicit Coulomb interactions yielded predictions of indistinguishable quality. Although short-range message passing is used to account for electrostatics, the atomic partial charge component of the AIMNet2 model was still retained because it serves as a coarse information source for the electron density distribution and is essential for distinguishing species with different charges. A demonstration of the high-quality assignment of AIMNet2 partial charges is provided in Figure , where a comparison of molecular dipole moments on external testing structures yields an R 2 of 0.99. Other than the treatment of electrostatics, all settings, such as sizes of neural networks, match the pre-trained AIMNet2 models. Parameterization was carried out for chemical species composed of elements from the set {H, B, C, N, O, F, Si, P, S, Cl, Se, Br, I, Pd}.