When human experts determine the structure of a single crystalline sample using electron diffraction, they first tilt the sample to some low-order zone axis and record a DP, and then tilt the sample to other low-order zone axes to collect additional DPs until they are confident in their conclusion about the crystal structure . Considering that artificial intelligence (AI) aims to mimic human intelligence, it follows that researchers would seek to develop ML algorithms guided by the operational principles of human brains for crystal system identification. Herein, we describe a multiview opinion fusion machine learning (MVOF-ML) framework for crystal system classification with DPs from multiple arbitrary zone axes that is compatible with the nanoparticle megalibrary platform and that does not require sample titling. Given the spotty distribution of diffraction peaks (Bragg disks; particularly for DPs from random, off-axis orientations), vectorized location and intensity information is used as the representation of the DPs instead of the traditional images. We show that this more compact representation of diffraction information leads to greater ML efficiency; a multistream CNN customized from PointNet 28 is utilized to handle this form of point cloud data. The CNN model takes on the role of a human expert making predictions (or forming opinions) from DPs with arbitrary zone axes. Our crystal system classification and its corresponding uncertainty are determined via a sequential decision-making process based on a series of sets of partial information from multiple views. The classifications ("opinions") and their related uncertainties are fused from different views, and these fused uncertainties reflect the confidence in making the final classification decision and guide whether to continue versus stop collecting additional DP input data. Our MVOF-ML framework achieves a high testing accuracy (percentage of correct predictions) of 0.94 on simulated data and is shown to be robust even in the presence of different sources of noise designed to mimic real experimental conditions. Finally, this framework is applied to experimental data obtained from 4D-STEM datasets of nanoparticle libraries (representative subsets of a megalibrary synthesized by Dip-Pen Nanolithography (DPN) , the precursor to PPL, where it performs with similar accuracy. Rather than applying off-the-shelf ML methodology, we built a custom CNN in which the convolution operations are modified in order to arrive at kernels that can be merged to incorporate specific physical principles embedded in the data (for example, radial and angular adjacency information, which encodes symmetry information in a DP). The success of the approach highlights the value of developing physics-informed network ML architectures and incorporating them into frameworks that mirror human scientific reasoning processes to accelerate data analysis and processing across multiple fields.