DPs with different zone axes were simulated for crystal structures from the Materials Project database . Since it is impossible to train the neural network using data from all possible zone axes, a number of diverse and representative zone axes were sampled and tested in 3D space (Fig. ). For training and testing, one DP from a random orientation is used as input. The classification results, shown in a confusion matrix, have true labels as well as predicted labels (Fig. ). The summation of each row is the total amount of data per class (i.e., 329,000). The diagonal numbers show correct classifications, and the off-diagonal numbers show misclassifications. For predictions made using single input DPs (one zone axis), the testing accuracy was 0.55. We hypothesized that the accuracy was low for two key reasons. First, any single input DP is only a 2D projection of a 3D crystal structure, and it may not contain sufficient information to uniquely classify the crystal system. Second, the CNN was trained using the data from a fixed and finite number of zone axes, so the zone axes of most of the testing data were different from those of the training data. To explore the former hypothesis, we trained and tested the CNN using two or three DPs (taken along different zone axes) as simultaneous inputs (vide infra, SI Fig. ), and the accuracy of the prediction made with multiple DP inputs is improved (0.94), which is expected as this provides the CNN with information in 3D. However, since the DPs will have arbitrary orientations for actual samples, this solution cannot be applied to improve accuracy in real high-throughput experiments. So, moving forward, we only input one DP at a time. In particular, we also tested the CNN using test cases comprised of a single DP input at the same orientations as the training data, and a higher accuracy of 0.78 (versus the 0.55 stated above) was attained (SI Fig. ). This implies that the CNN is more accurate in classifying a DP if the orientation is similar to one that it has already seen. This bodes well because we note that our CNN architecture is not designed to achieve the highest accuracy for a one-shot prediction. Instead, it is designed to achieve a high accuracy as data accumulates, so that it is meaningful in the context of high-throughput experimental data acquisition. We present classification results obtained after fusing information from multiple zone axes in the next section.