Progress in the field of automated materials discovery relies on high-throughput experimentation geared towards the rapid identification of key structure-function relationships that define material properties (Fig. ). Recently, polymer pen lithography (PPL) , a cantilever-free approach to synthesizing nanoparticle megalibraries, has enabled the synthesis of billions of unique nanomaterials in a single experiment . Nevertheless, the throughput of this approach, which relies on 90,000 pyramidal tips to independently synthesize distinct materials, far outpaces the rate of characterization (via screening) and analysis. Four-dimensional scanning transmission electron microscopy (4D-STEM) is ideally suited for identifying crystal structures on the nanometer scale at arbitrary locations on a specimen, including specimens that are nanoparticles in a megalibrary on a substrate. 4D-STEM allows spatially resolved electron diffraction patterns (DPs) to be collected. The structural information gleaned from such DPs is critical in identifying nanoparticle structure-function relationships (because symmetry determines the number of independent components of property tensors). Experimental structure data from DPs are also amenable to conversion to computation-ready data as inputs for computational modeling. However, the identification of the type of crystal systems (e.g., triclinic, monoclinic, etc.) from large 4D-STEM datasets is not trivial; it is impossible to manually extract structural information from every single DP, due to the volume and complexity of the data and data processing.