Autonomous experimentation , which integrates machine learning (ML)-driven interpretation of data collected in previous measurements to guide subsequent measurements, requires, in the case of DPs, the rapid, automated extraction of crystallographic information in real-time. Indeed, ML methods are capable of extracting information from complex and unstructured datasets and have proven useful in the analysis of large volumes of data for pattern recognition and crystal structure identification from DPs . Convolutional neural networks (CNNs) are among the most widely used ML methods for evaluating diffraction data in image format, and they have been applied to crystal structure identification from two-dimensional (2D) and one-dimensional (1D) diffraction data . Still, the use of CNN models in the context of DPs is limited; the data must be collected from specific low-order zone axes (high-symmetry orientations in a crystal that contain the most information) in order to maximize the amount of information in the DP and facilitate its analysis by both humans and machines. However, for these models to be accurate, the crystal DPs must be obtained from the same zone axes as the training data, which is not possible in every experimental system. For example, it is not feasible to physically tilt samples to specific zone axes in most high-throughput experimental workflows, including in the context of nanoparticle megalibraries, to collect DPs with known zone axes. Thus, the zone axes of the DPs obtained from 4D-STEM experiments on nanoparticle megalibraries are unknown, and so cannot be matched with the appropriate training data. Moreover, a single DP usually provides only 2D (projected) crystallographic information. To obtain a full description of a material in three dimensions (3D), it is typically necessary to observe the crystal from different orientations via tilting experiments (Fig. ). In light of these issues, in order to implement autonomous ML-based methods for high-throughput structural screening of vast nanomaterial libraries, it is necessary to develop ML models that can interpret DP information from arbitrary orientations to permit the direct reconstruction of 3D structural information without the need to physically tilt the sample.