Here, the problems of data scarcity and limited structuresynthesis design rules are addressed through a high-throughput simulation study of over ten thousand new ligands and their stability in 2D halide perovskites. This has been accom-plished using algorithmic ligand generation to dramatically broaden the scope of interrogated ligand chemistries, an automated MD simulation workflow for equilibrating structures and implementing generalizable geometric criteria for quantifying the structural stability of the resulting perovskites. 10899 ligands were characterized using this framework. To demonstrate the generality of the design rules embedded in this dataset, several machine learning classifiers were trained against it and used to predict the stability of unseen ligands. The resulting models can predict perovskite stability based solely on ligand features, thus circumventing the need for direct simulations and validating the general design relationships embedded in this new ligand dataset. To establish that these results can be translated into real materials, four unseen and novel ligands were tested against the best ML classifier then experimentally synthesized and incorporated into 2D perovskites.