Three-dimensional (3D) halide perovskites with the general formula of AMX3 [A = CH3NH + 3 (MA), or HC(NH2)2 + (FA); M = Ge 2+ , Sn 2+ , Pb 2+ ; X= Cl -, Br -, I -] consist of a 3D network of corner-sharing metal (M) halide (X) octahedra. Nevertheless, these structures can only accommodate small cations and thus have a limited design space. In contrast, two-dimensional (2D) halide perovskites, which can be regarded as analogs of 3D halide perovskites cleaved along a plane to form sheets, can accommodate large organic cations at their surfaces with potentially broad tunability. 2D perovskite phases are consequential for many applications, whether employed as bulk 2D active layers or as 2D phases acting at the surface of 3D perovskite films. For instance, the comparative stability of 2D analogs of 3D lead-halide perovskites has made them leading candidates for perovskite photovoltaic interfacial layers in solar cells. 2D perovskite films are also of interest as stand-alone active layers for LED devices due to the relatively large exciton binding energy imparted by their confined quantum-well structure. Despite the potential design space of 2D halide perovskites, relatively few ligands (<100) have been incorporated into these materials. Reported ligands mainly comprise simple aliphatic chains and ring structures with limited functionality. Additional synthetic exploration has been piecemeal, with studies characteristically reporting a few new ligands in the context of a particular application. Some important factors regarding ligand design that have been indirectly established from these studies include the size of the ligand in comparison with the lattice dimensions, the adverse effect of steric substitutions proximate to the cation moiety, ethyl linkers and ammonium head groups as default choices, and the use of methyl substitutions to inhibit the crystallization of highly conjugated ligands. Nevertheless, there has been no general study of the interplay between these (or other) factors or of what fundamental constraints are placed on the ligand for them to fit in the lattice and form the desired phase under typical fabrication conditions. An initial step was recently taken in this direction by Lyu et al., who trained an L1 regularized logistic regression model to quantify the importance of different ligand factors and predict the perovskite dimensionality of untested ligands. These results relied on a small number of experimentally reported ligands (86), which severely constrained the predictive power of the model. Published ligand data is scarce, lacks structural diversity, and also exhibits a strong bias toward positive examples (i.e., ligands that fail to form perovskites are rarely reported). While there has been other simulation and machine learning work to predict perovskite electronic properties or predict dimensionality, there has been comparatively little dedicated to understanding the atomistic ligand factors that influence stability. There are thus clear opportunities to generate more diverse ligand data and establish ligand design constraints.