After training, the CNN can be applied to make predictions when given a DP with any arbitrary zone axis. The MVOF-ML framework (Fig. ) is implemented and its power is illustrated (Fig. ) using a triclinic LiCo(PO 3 ) 3 bulk structure randomly selected from Materials Project database (mp-26015) as an example. First, a DP orientation is randomly selected (zone axis [2 1 4]). The classification result given by the CNN model shows that the most probable classification is trigonal. The uncertainty mass for the first DP is 0.39. The uncertainty mass is higher than the threshold of 0.1, and so the second DP is collected. This representation is analogous to human reasoning, where a decision cannot be made if one is not confident in the conclusion. The CNN model classifies the second DP with another randomly selected zone axis, [2 1 0], as triclinic with an uncertainty mass of 0.22. The fused opinion indicates that both triclinic and trigonal are possible. The fused uncertainty mass is 0.16, which is still higher than the threshold of 0.1, so another DP with a random zone axis is analyzed, and the process continues until the fused uncertainty is lower than the threshold. The final fused opinion yields a decision that the crystal symmetry is triclinic, the correct decision, after five iterations. This example highlights that, although a CNN model may misclassify a single DP, the opinion fusion framework makes the correct overall decision in the end after the uncertainty decreases to a level where the CNN is confident in the opinion.