The identification plots of the 17 nanoparticles are presented in SI Fig. . Each nanoparticle varies in shape and composition of grains. The locations and intensities of the Bragg disks are extracted from the DPs. The locations are represented as the centers of the Bragg disks. Since the true Bragg disk intensities are unknown for the experimental data, we approximate intensities using the crosscorrelation intensities that measure the extent that a point is a true Bragg disk. The process to extract the data for a typical nanoparticle contains local averaging, symmetrizing, background subtraction, Bragg disk identification, calibration and recentering (SI Fig. ). Since the raw DPs of each scan position from the 4D-STEM datasets are noisy, we averaged DPs of each scan position for analysis to reduce the noise. In the end, the coordinates are centered and transformed from pixel distances to inverse nanometers. Inspired by the human reasoning process, the framework for automated crystal system identification for 4D-STEM is developed and presented (Fig. ), and the algorithm is shown in Algorithm 1. First, a machine learning model (a modified version of PointNet CNN in this work) is trained to make predictions in place of a human expert. Next, a DP simulated from an arbitrary orientation is used as input in the CNN. The CNN predicts the probability that a crystal belongs to each crystal system, regarded as one opinion. In addition to an opinion about what probability it assigns to each class, we also determine how confident it is in that opinion (i.e., the uncertainty quantification) . Next, another DP is collected from a different arbitrary orientation, and the same process is conducted to form the second opinion as well as its uncertainty. Following that, the first and second opinions are fused, and their uncertainties are also fused. If the fused uncertainty is still high, another new opinion will be formed from an additional DP. The previously fused opinion and uncertainty will be fused with the new opinion and uncertainty. Fused Opinion II is more confident (less uncertain) about what structure the crystal belongs to as information is accumulated (Fig. ). That process is repeated until the fused uncertainty is below a sufficiently small, specified threshold. In other words, when enough DPs are analyzed, decisions regarding the crystal system can be finally made with a high confidence. The decision is the final fused opinion. The uncertainty threshold in Algorithm 1 is a user-defined value. A lower threshold corresponds to a more conservative decision.