where 𝑝 𝑘 is the probability that the CNN model assigns to the 𝑘-th class. A higher 𝑢 or 𝐻 implies that the model is more uncertain about its prediction. For each test case, 𝑢 and 𝐻 are calculated and the distributions of all test cases are presented (Fig. ). Both the mean uncertainty mass and the mean entropy are higher for misclassifications than for correct classifications (Fig. ). This result is reasonable because the model is more uncertain on the samples it predicts wrongly. The testing data is further categorized according to seen and unseen zone axes. The model is more confident when the zone axes were already seen by the model (i.e., they were used for training) (Fig. ). Again, the data show that the CNN trained with evidential deep learning is aware of the uncertainty in its predictions, which are leveraged to guide the decision-making process while the DPs are accumulated. (accuracy 0.71). For other crystal systems, the accuracy is between 0.95 and 1.00. The total amount of data for each class is different from that seen in Fig. . This is because, in this case, we treated each crystal as one sample after opinion fusion, while above we treated the DPs from different zone axes of a crystal as separate datapoints before fusion. g, The testing process for each crystal is repeated multiple times. An arbitrary series of DPs with different zone axes are used for opinion fusion at each time. The final decision is the class with the majority vote. The vote percentages are higher for correct classifications than misclassifications. Thus, a correct decision is associated with high confidence.