The perfect outcome of this small synthetic trial should not be overinterpreted since the ML model is still subject to the extrapolation uncertainties that are expected from the simple featurization, the ML model architecture, and the underlying accuracy of the MD training data. Nonetheless, this high baseline performance on unseen ligands suggests that the opportunities for ML-guided ligand design are large and will only improve with more sophisticated models and more diverse datasets. The relative ease with which the synthesizability prediction task was learned also makes it plausible that other ligand and perovskite properties might be simultaneously optimized and predicted with a comparably diverse dataset. A foreseeable opportunity is to expand the dataset to include structures with n>1 (where octahedral tilt among other factors, plays a more significant role in stability), other metals, and other halides. The range of properties can also be expanded to include thermodynamic stability measures, like the heat of formation, and optoelectronic properties, like ligand-perovskite electronic couplings and carrier mobilities. The availability of electronic structure data for training ML-based force fields specific to 2Dperovskites would also overcome the current reliance on simple force-fields for MD data generation. For example, better force-fields would be required for a quantitative description of hydrogen bonding, which has been invoked to rationalize stability relationships in several case studies. Despite these improvement opportunities, the current work shows that a data-led paradigm for ligand design is already possible with available tools and capable of avoiding costly experimental trial-and-error in selecting ligands.