The MVOF-ML framework utilizes a custom CNN architecture that incorporates the physics of the system captured implicitly by the DP (Fig. ). Our CNN uses vector maps (Fig. ), which contain point cloud data with the locations (r, θ) and intensities (I) of the Bragg disks, as inputs, as opposed to images used in other ML models. There are three branches before a fully connected layer, for which the inputs are the vector maps corresponding to the same DP, sorted in different orders r-θ-I, θ-r-I, and I-r-θ, respectively (Fig. ) (r-θ-I represents the vector map sorted first by r, then θ, and then intensity I, for example). The sorting of the vector map affects the ability of the following convolutional layer to learn how adjacent Bragg disks, which encode structural symmetry, are correlated. The kernels defining adjacency in the convolutional layers are illustrated by the rectangles inside the DPs (Fig. ), and they are also shown in black squares containing two rows in the vector maps (Fig. ). The PointNet 28 framework has been widely used for deep learning on point sets for 3D classifications, thus it is expected that this CNN would work well as a starting point with the 3D point representation (r, θ and I) of Bragg disks in a DP. We modified this standard PointNet CNN architecture according to the physics encapsulated in the point cloud inputs (r, θ, and I) (Fig. ), using appropriately structured kernels to incorporate the physical adjacency information directly into the network architecture. This modified PointNet CNN is then applied to each input vector map that is gleaned from subsequent DPs. At the end, an output layer contains seven neurons corresponding to the seven crystal systems (Fig. ). The DPs in Fig. correspond to crystal orientations in Fig. with the same frame color. The increase of opacity in option/decision blocks in the bottom two rows in Fig. indicates the increase of confidence (or the decrease of uncertainty). Thirty-four zone axes were sampled for training and 94 were sampled for testing.