Next, the overall predictive performance of our CNN framework was examined on the whole testing data set from the Materials Project database (see Methods). The confusion matrix is shown in Fig. . The overall accuracy is 0.94, which is higher than other models in the current literature used for crystal system classification. All cubic crystals are correctly identified (i.e., the accuracy is 1) (Fig. ), and the accuracy was the lowest for the triclinic (accuracy 0.71). For the other crystal systems, their accuracies were between 0.95 and 1.00. If we do not leverage uncertainty decision-making in our framework, fusing the opinions for the DPs of all 94 testing zone axes for a single crystal, the accuracy is 0.54. This accuracy is similar to that seen before the opinion fusion (single DP input, see Section 2.2). These results highlight that the uncertainty quantification is just as important as the number of informational inputs in the ultimate decision-making process. Our CNN operates analogous to a human assigning different degrees of "trust" to an opinion depending on the degree of uncertainty associated with it.